21.7.3.8 Pulmonary Nodules, Lung Nodules

Chapter Contents (Back)
Pulmonary Nodules. Lungs. Lung Nodules. Medical, Applications.

Engvall, J.L.[John L.], Greenberg, S.D., Spjut, H.J., Estrada, R., Subach, J., Kimzey, S.L., King, J.F., DiTrapani, P.M.,
Development of a mathematical model to analyze color and density as discriminant features for pulmonary squamous epithelial cells,
PR(13), No. 1, 1981, pp. 37-47.
Elsevier DOI 0309
BibRef

Lin, J.S.[Jyh-Shyan], Lo, S.C.B., Hasegawa, A., Freedman, M.T., Mun, S.K.,
Reduction of false positives in lung nodule detection using a two-level neural classification,
MedImg(15), No. 2, April 1996, pp. 206-217.
IEEE Top Reference. 0203
BibRef

Lee, Y.B.[Yong-Bum], Hara, T., Fujita, H., Itoh, S., Ishigaki, T.,
Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique,
MedImg(20), No. 7, July 2001, pp. 595-604.
IEEE Top Reference. 0110
BibRef

Brown, M.S., McNitt-Gray, M.F., Goldin, J.G., Suh, R.D., Sayre, J.W., Aberle, D.R.,
Patient-specific models for lung nodule detection and surveillance in CT images,
MedImg(20), No. 12, December 2001, pp. 1242-1250.
IEEE Top Reference. 0201
BibRef

Kostis, W.J., Reeves, A.P., Yankelevitz, D.F., Henschke, C.I.,
Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images,
MedImg(22), No. 10, October 2003, pp. 1259-1274.
IEEE Abstract. 0310
BibRef

Paik, D.S., Beaulieu, C.F., Rubin, G.D., Acar, B., Jeffrey, R.B., Yee, J., Dey, J., Napel, S.,
Surface Normal Overlap: A Computer-Aided Detection Algorithm with Application to Colonic Polyps and Lung Nodules in Helical CT,
MedImg(23), No. 6, June 2004, pp. 661-675.
IEEE Abstract. 0406
BibRef

Okada, K.[Kazunori], Comaniciu, D.[Dorin], Krishnan, A.[Arun],
Robust Anisotropic Gaussian Fitting for Volumetric Characterization of Pulmonary Nodules in Multislice CT,
MedImg(24), No. 3, March 2005, pp. 409-423.
IEEE Abstract. 0501
BibRef
Earlier:
Scale Selection for Anisotropic Scale-Space: Application to Volumetric Tumor Characterization,
CVPR04(I: 594-601).
IEEE DOI 0408
BibRef

Okada, K.[Kazunori], Akdemir, U.[Umut], Krishnan, A.[Arun],
Blob Segmentation Using Joint Space-Intensity Likelihood Ratio Test: Application to 3D Tumor Segmentation,
CVPR05(II: 437-444).
IEEE DOI 0507
BibRef

Okada, K.[Kazunori], Comaniciu, D.[Dorin], Dalal, N.[Navneet], Krishnan, A.[Arun],
A Robust Algorithm for Characterizing Anisotropic Local Structures,
ECCV04(Vol I: 549-561).
Springer DOI 0405
BibRef

Okada, K.[Kazunori], Singh, M.[Maneesh], Ramesh, V.[Visvanathan],
Prior-Constrained Scale-Space Mean Shift,
BMVC06(II:829).
PDF File. 0609
Semi-automatic 3D segmentation of lung nodules in CT data. BibRef

Suzuki, K., Li, F., Sone, S., Doi, K.,
Computer-Aided Diagnostic Scheme for Distinction Between Benign and Malignant Nodules in Thoracic Low-Dose CT by Use of Massive Training Artificial Neural Network,
MedImg(24), No. 9, September 2005, pp. 1138-1150.
IEEE DOI 0509
BibRef

Agam, G., Armato, III, S.G., Wu, C.,
Vessel Tree Reconstruction in Thoracic CT Scans With Application to Nodule Detection,
MedImg(24), No. 4, April 2005, pp. 486-499.
IEEE Abstract. 0501
BibRef

Reeves, A.P., Chan, A.B., Yankelevitz, D.F., Henschke, C.I., Kressler, B., Kostis, W.J.,
On Measuring the Change in Size of Pulmonary Nodules,
MedImg(25), No. 4, April 2006, pp. 435-450.
IEEE DOI 0604
BibRef

Campadelli, P., Casiraghi, E., Artioli, D.,
A Fully Automated Method for Lung Nodule Detection From Postero-Anterior Chest Radiographs,
MedImg(25), No. 12, December 2006, pp. 1588-1603.
IEEE DOI 0701
BibRef

Dehmeshki, J., Amin, H., Valdivieso, M., Ye, X.J.[Xu-Jiong],
Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach,
MedImg(27), No. 4, April 2008, pp. 467-480.
IEEE DOI 0804
BibRef

Dehmeshki, J., Ye, X.J.[Xu-Jiong], Costello, J.,
Shape based region growing using derivatives of 3D medical images: application to semi-automated detection of pulmonary nodules,
ICIP03(I: 1085-1088).
IEEE DOI 0312
BibRef

Takizawa, H.[Hotaka], Shigemoto, K.[Kanae], Yamamoto, S.[Shinji], Matsumoto, T.[Tohru], Tateno, Y.[Yukio], Iinuma, T.[Takeshi], Matsumoto, M.[Mitsuomi],
A Recognition Method of Lung Nodule Shadows in X-ray Ct Images Using 3d Object Models,
IJIG(3), No. 4, October 2003, pp. 533-545. 0310
BibRef

El-Baz, A.[Ayman], Gimel'farb, G.L.[Georgy L.], Falk, R.[Robert], El-Ghar, M.A.[Mohamed Abo],
Automatic analysis of 3D low dose CT images for early diagnosis of lung cancer,
PR(42), No. 6, June 2009, pp. 1041-1051.
Elsevier DOI 0902
BibRef
Earlier:
A new approach for automatic analysis of 3D low dose CT images for accurate monitoring the detected lung nodules,
ICPR08(1-4).
IEEE DOI 0812
BibRef
Earlier:
A New CAD System for Early Diagnosis of Detected Lung Nodules,
ICIP07(II: 461-464).
IEEE DOI 0709
BibRef
And:
A Novel Approach for Automatic Follow-Up of Detected Lung Nodules,
ICIP07(V: 501-504).
IEEE DOI 0709
Computed tomography; Growth rate estimation; Global registration; Local registration; Segmentation; Pulmonary nodules; Early diagnosis; Lung cancer
See also New CAD System for Early Diagnosis of Dyslexic Brains, A. BibRef

El-Baz, A., Sethu, P., Gimel'farb, G.L., Khalifa, F., Elnakib, A., Falk, R., El-Ghar, M.A.[M. Abo],
A new validation approach for the growth rate measurement using elastic phantoms generated by state-of-the-art microfluidics technology,
ICIP10(4381-4384).
IEEE DOI 1009
For early diagnosis of pulmonary nodules. BibRef

El-Baz, A.[Ayman], Farag, A.A.[Aly A.], Ali, A.M.[Asem M.], Gimel'farb, G.L.[Georgy L.], Casanova, M.[Manuel],
A Framework for Unsupervised Segmentation of Multi-modal Medical Images,
CVAMIA06(120-131).
Springer DOI 0605
BibRef

El-Baz, A.[Ayman], Farag, A.A.[Aly A.], Gimel'farb, G.L.[Georgy L.], Falk, R.[Robert], El-Ghar, M.A.[Mohamed A.], Eldiasty, T.[Tarek],
A Framework for Automatic Segmentation of Lung Nodules from Low Dose Chest CT Scans,
ICPR06(III: 611-614).
IEEE DOI 0609
BibRef

El-Baz, A.[Ayman], Gimel'farb, G.L.[Georgy L.], El-Ghar, M.A.[Mohamed Abou], Falk, R.[Robert],
Appearance-based diagnostic system for early assessment of malignant lung nodules,
ICIP12(533-536).
IEEE DOI 1302
BibRef

Farag, A., Gimel'farb, G.L.[Georgy L.], El-Baz, A.[Ayman], Falk, R.[Robert],
Detection and recognition of lung nodules in spiral CT images using deformable templates and Bayesian post-classification,
ICIP04(V: 2921-2924).
IEEE DOI 0505
BibRef
And:
Detection and recognition of lung abnormalities using deformable templates,
ICPR04(III: 738-741).
IEEE DOI 0409
BibRef

van Rikxoort, E.M., de Hoop, B., van de Vorst, S., Prokop, M., van Ginneken, B.,
Automatic Segmentation of Pulmonary Segments From Volumetric Chest CT Scans,
MedImg(28), No. 4, April 2009, pp. 621-630.
IEEE DOI 0904
BibRef

Pu, J.T.[Jian-Tao], Leader, J.K., Zheng, B.[Bin], Knollmann, F., Fuhrman, C., Sciurba, F.C., Gur, D.,
A Computational Geometry Approach to Automated Pulmonary Fissure Segmentation in CT Examinations,
MedImg(28), No. 5, May 2009, pp. 710-719.
IEEE DOI 0905
BibRef

Pu, J.T.[Jian-Tao], Zheng, B., Leader, J.K., Fuhrman, C., Knollmann, F., Klym, A., Gur, D.,
Pulmonary Lobe Segmentation in CT Examinations Using Implicit Surface Fitting,
MedImg(28), No. 12, December 2009, pp. 1986-1996.
IEEE DOI 0912
BibRef

Pu, J.T.[Jian-Tao], Fuhrman, C., Good, W.F., Sciurba, F.C., Gur, D.,
A Differential Geometric Approach to Automated Segmentation of Human Airway Tree,
MedImg(30), No. 2, February 2011, pp. 266-278.
IEEE DOI 1102
BibRef

Diciotti, S., Lombardo, S., Coppini, G., Grassi, L., Falchini, M., Mascalchi, M.,
The LoG Characteristic Scale: A Consistent Measurement of Lung Nodule Size in CT Imaging,
MedImg(29), No. 2, February 2010, pp. 397-409.
IEEE DOI 1002
BibRef

Wu, D.[Dijia], Lu, L.[Le], Bi, J.[Jinbo], Shinagawa, Y.[Yoshihisa], Boyer, K.L.[Kim L.], Krishnan, A.[Arun], Salganicoff, M.[Marcos],
Stratified learning of local anatomical context for lung nodules in CT images,
CVPR10(2791-2798).
IEEE DOI 1006
BibRef

Gavrielides, M.A., Zeng, R.[Rongping], Kinnard, L.M., Myers, K.J., Petrick, N.,
Information-Theoretic Approach for Analyzing Bias and Variance in Lung Nodule Size Estimation With CT: A Phantom Study,
MedImg(29), No. 10, October 2010, pp. 1795-1807.
IEEE DOI 1011
BibRef

Lee, S.L.A., Kouzani, A.Z., Hu, E.J.,
Automated detection of lung nodules in computed tomography images: a review,
MVA(23), No. 1, January 2012, pp. 151-163.
WWW Link. 1201
BibRef

Netto, S.M.B.[Stelmo Magalhães Barros], Silva, A.C.[Aristófanes Corrêa], Nunes, R.A.[Rodolfo Acatauassú], Gattass, M.[Marcelo],
Analysis of directional patterns of lung nodules in computerized tomography using Getis statistics and their accumulated forms as malignancy and benignity indicators,
PRL(33), No. 13, 1 October 2012, pp. 1734-1740.
Elsevier DOI 1208
Medical image; Computer-aided diagnosis (CADx); Lung nodules; Getis? statistics; Image processing BibRef

Bab-Hadiashar, A.[Alireza], Tennakoon, R.B.[Ruwan B.], de-Bruijne, M.[Marleen],
Quantification of Smoothing Requirement for 3D Optic Flow Calculation of Volumetric Images,
IP(22), No. 6, 2013, pp. 2128-2137.
IEEE DOI 1307
dynamic lung CT imaging; 3D optic flow BibRef

Tennakoon, R.B.[Ruwan B.], Bab-Hadiashar, A.[Alireza], Cao, Z., de-Bruijne, M.[Marleen],
Nonrigid Registration of Volumetric Images Using Ranked Order Statistics,
MedImg(33), No. 2, February 2014, pp. 422-432.
IEEE DOI 1403
computerised tomography BibRef

Tennakoon, R.B.[Ruwan B.], Bortsova, G., Ørting, S., Gostar, A.K.[Amirali K.], Wille, M.M.W., Saghir, Z., Hoseinnezhad, R.[Reza], de-Bruijne, M.[Marleen], Bab-Hadiashar, A.[Alireza],
Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem,
MedImg(39), No. 4, April 2020, pp. 854-865.
IEEE DOI 2004
BibRef
Earlier: A1, A4, A7, A8, A9, Only:
Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions,
ACCV18(III:590-604).
Springer DOI 1906
Training, Medical diagnostic imaging, Task analysis, Training data, Pathology, Multiple instance learning, COPD BibRef

Jung, Y.H.[Youn-Hyun], Kim, J.M.[Jin-Man], Eberl, S.[Stefan], Fulham, M.J.[Micheal J.], Feng, D.D.[David Dagan],
Visibility-driven PET-CT visualisation with region of interest (ROI) segmentation,
VC(29), No. 6-8, June 2013, pp. 805-815.
Springer DOI 1306
BibRef

Ballangan, C.[Cherry], Wang, X.Y.[Xiu-Ying], Fulham, M.J.[Michael J.], Eberl, S.[Stefan], Feng, D.D.[David Dagan],
Lung tumor delineation in PET-CT images using a downhill region growing and a Gaussian mixture model,
ICIP11(2173-2176).
IEEE DOI 1201
BibRef

Lin, P.L.[Phen-Lan], Huang, P.W.[Po-Whei], Lee, C.H.[Cheng-Hsiung], Wu, M.T.[Ming-Ting],
Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model,
PR(46), No. 12, 2013, pp. 3279-3287.
Elsevier DOI 1308
Classification BibRef

Mi, H., Petitjean, C., Dubray, B., Vera, P., Ruan, S.,
Prediction of Lung Tumor Evolution During Radiotherapy in Individual Patients With PET,
MedImg(33), No. 4, April 2014, pp. 995-1003.
IEEE DOI 1404
Brain modeling BibRef

Ciompi, F., Jacobs, C., Scholten, E.T., Wille, M.M.W., de Jong, P.A., Prokop, M., van Ginneken, B.,
Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images,
MedImg(34), No. 4, April 2015, pp. 962-973.
IEEE DOI 1504
Biomedical imaging BibRef

Song, J., Yang, C., Fan, L., Wang, K., Yang, F., Liu, S., Tian, J.,
Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach,
MedImg(35), No. 1, January 2016, pp. 337-353.
IEEE DOI 1601
Accuracy BibRef

Setio, A.A.A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S.J., Wille, M.M.W., Naqibullah, M., Sánchez, C.I., van Ginneken, B.,
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks,
MedImg(35), No. 5, May 2016, pp. 1160-1169.
IEEE DOI 1605
Cancer BibRef

Dhara, A.K., Mukhopadhyay, S., Chakrabarty, S., Garg, M., Khandelwal, N.,
Quantitative evaluation of margin sharpness of pulmonary nodules in lung CT images,
IET-IPR(10), No. 9, 2016, pp. 631-637.
DOI Link 1609
cancer BibRef

Shen, W.[Wei], Zhou, M.[Mu], Yang, F.[Feng], Yu, D.D.[Dong-Dong], Dong, D.[Di], Yang, C.Y.[Cai-Yun], Zang, Y.[Yali], Tian, J.[Jie],
Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification,
PR(61), No. 1, 2017, pp. 663-673.
Elsevier DOI 1705
Lung nodule BibRef

Cirujeda, P.[Pol], Müller, H.[Henning], Rubin, D.[Daniel], Aguilera, T.A.[Todd A.], Loo, B.W.[Billy W.], Diehn, M.[Maximilian], Binefa, X.[Xavier], Depeursinge, A.[Adrien],
A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT,
MedImg(35), No. 12, December 2016, pp. 2620-2630.
IEEE DOI 1612
Biomedical imaging BibRef

Dicente Cid, Y., Müller, H., Platon, A., Poletti, P.A., Depeursinge, A.,
3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms,
IP(26), No. 4, April 2017, pp. 1899-1910.
IEEE DOI 1704
Biomedical imaging BibRef

Tajbakhsh, N.[Nima], Suzuki, K.[Kenji],
Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs,
PR(63), No. 1, 2017, pp. 476-486.
Elsevier DOI 1612
Deep learning BibRef

Cao, P.[Peng], Liu, X.L.[Xiao-Li], Yang, J.Z.[Jin-Zhu], Zhao, D.[Dazhe], Li, W.[Wei], Huang, M.[Min], Zaiane, O.[Osmar],
A multi-kernel based framework for heterogeneous feature selection and over-sampling for computer-aided detection of pulmonary nodules,
PR(64), No. 1, 2017, pp. 327-346.
Elsevier DOI 1701
Lung nodule detection BibRef

Chen, S., Qin, J., Ji, X., Lei, B., Wang, T., Ni, D., Cheng, J.Z.,
Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images,
MedImg(36), No. 3, March 2017, pp. 802-814.
IEEE DOI 1703
Computational modeling BibRef

Liu, K.[Kui], Kang, G.X.[Gui-Xia],
Multiview convolutional neural networks for lung nodule classification,
IJIST(27), No. 1, 2017, pp. 12-22.
DOI Link 1704
lung nodule classification BibRef

Farhangi, M.M., Frigui, H., Seow, A., Amini, A.A.,
3-D Active Contour Segmentation Based on Sparse Linear Combination of Training Shapes (SCoTS),
MedImg(36), No. 11, November 2017, pp. 2239-2249.
IEEE DOI 1711
Active contours, Cancer, Image segmentation, Level set, Lungs, Shape, Adaptive shape prior, X-ray CT, dictionary learning, level set segmentation, lung nodules, sparse, representation BibRef

Zhang, W., Song, Y., Chen, Y., Ma, J., Sun, J., Zhao, J.,
Limited-Range Few-View CT: Using Historical Images for ROI Reconstruction in Solitary Lung Nodules Follow-up Examination,
MedImg(36), No. 12, December 2017, pp. 2569-2577.
IEEE DOI 1712
Lung nodule follow-up, radiation reduction, the first CT scans BibRef

Liu, X.L.[Xing-Long], Hou, F.[Fei], Qin, H.[Hong], Hao, A.[Aimin],
Multi-view multi-scale CNNs for lung nodule type classification from CT images,
PR(77), 2018, pp. 262-275.
Elsevier DOI 1802
Computed tomography, Lung nodule, CNNs BibRef

Li, X.X.[Xiang-Xia], Li, B.[Bin], Tian, L.F.[Lian-Fang], Zhang, L.[Li],
Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm,
IET-IPR(12), No. 7, July 2018, pp. 1253-1264.
DOI Link 1806
BibRef

Xie, H.T.[Hong-Tao], Yang, D.B.[Dong-Bao], Sun, N.N.[Nan-Nan], Chen, Z.N.[Zhi-Neng], Zhang, Y.D.[Yong-Dong],
Automated Pulmonary Nodule Detection in CT Images Using Deep Convolutional Neural Networks,
PR(85), 2019, pp. 109-119.
Elsevier DOI 1810
Nodule detection, Convolutional neural network, False positive reduction, Computer-aided diagnosis BibRef

Jiang, J., Hu, Y., Liu, C., Halpenny, D., Hellmann, M.D., Deasy, J.O., Mageras, G., Veeraraghavan, H.,
Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images,
MedImg(38), No. 1, January 2019, pp. 134-144.
IEEE DOI 1901
Image resolution, Tumors, Streaming media, Lung, Cancer, Feature extraction, Image segmentation, Deep learning, detection BibRef

Gerard, S.E., Patton, T.J., Christensen, G.E., Bayouth, J.E., Reinhardt, J.M.,
FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images,
MedImg(38), No. 1, January 2019, pp. 156-166.
IEEE DOI 1901
Lung, Computed tomography, Image segmentation, Feature extraction, Detectors, Machine learning, Training, Lung, segmentation, CNN BibRef

Huidrom, R.[Ratishchandra], Chanu, Y.J.[Yambem Jina], Singh, K.M.[Khumanthem Manglem],
Pulmonary nodule detection on computed tomography using neuro-evolutionary scheme,
SIViP(13), No. 1, February 2019, pp. 53-60.
Springer DOI 1901
BibRef

Zhang, Z.C.[Zhan-Cheng], Li, X.Y.[Xin-Yi], You, Q.J.[Qing-Jun], Luo, X.Q.[Xiao-Qing],
Multicontext 3D residual CNN for false positive reduction of pulmonary nodule detection,
IJIST(29), No. 1, March 2019, pp. 42-49.
WWW Link. 1902
BibRef

Xie, Y., Xia, Y., Zhang, J., Song, Y., Feng, D., Fulham, M., Cai, W.,
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT,
MedImg(38), No. 4, April 2019, pp. 991-1004.
IEEE DOI 1904
Lung, Cancer, Feature extraction, Computed tomography, Shape, Machine learning, computed tomography (CT) BibRef

Moghaddam, A.E.[Amal Eisapour], Akbarizadeh, G.[Gholamreza], Kaabi, H.[Hooman],
Automatic detection and segmentation of blood vessels and pulmonary nodules based on a line tracking method and generalized linear regression model,
SIViP(13), No. 3, April 2019, pp. 457-464.
Springer DOI 1904
BibRef

Roy, R.[Rukhmini], Chakraborti, T.[Tapabrata], Chowdhury, A.S.[Ananda S.],
A deep learning-shape driven level set synergism for pulmonary nodule segmentation,
PRL(123), 2019, pp. 31-38.
Elsevier DOI 1906
Lung nodule segmentation, Level sets, Shape information, Convolutional neural networks BibRef

Roy, R.[Rukhmini], Banerjee, P., Chowdhury, A.S.[Ananda S.],
A Level Set Based Unified Framework for Pulmonary Nodule Segmentation,
SPLetters(27), 2020, pp. 1465-1469.
IEEE DOI 2009
Level set, Shape, Lung, Solids, Image segmentation, Glass, Machine learning, Pulmonary nodule segmentation, level sets, contrast adaptation BibRef

Shakoor, M.H.[Mohammad Hossein],
Lung tumour detection by fusing extended local binary patterns and weighted orientation of difference from computed tomography,
IET-IPR(13), No. 6, 10 May 2019, pp. 877-884.
DOI Link 1906
BibRef

Theodorakis, L., Loudos, G., Prassopoulos, V., Kappas, C., Tsougos, I., Georgoulias, P.,
PET Counting Response Variability Depending on Tumor Location, Activity, and Patient Obesity: A Feasibility Study of Solitary Pulmonary Nodule Using Monte Carlo,
MedImg(38), No. 7, July 2019, pp. 1763-1774.
IEEE DOI 1907
Tumors, Detectors, Biological system modeling, Data models, Positron emission tomography, Obesity, Biographies, nuclear imaging BibRef

Sathiya, T.[Thanikachalam], Sathiyabhama, B.[Balasubramaniam],
Fuzzy relevance vector machine based classification of lung nodules in computed tomography images,
IJIST(29), No. 3, September 2019, pp. 360-373.
DOI Link 1908
BibRef

Paulraj, T.[Tharcis], Chelliah, K.S.V.[Kezi Selva Vijila], Chinnasamy, S.[Sundar],
Lung computed axial tomography image segmentation using possibilistic fuzzy C-means approach for computer aided diagnosis system,
IJIST(29), No. 3, September 2019, pp. 374-381.
DOI Link 1908
BibRef

Zheng, S., Guo, J., Cui, X., Veldhuis, R.N.J., Oudkerk, M., van Ooijen, P.M.A.,
Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection,
MedImg(39), No. 3, March 2020, pp. 797-805.
IEEE DOI 2004
Maximum intensity projection (MIP), convolutional neural network (CNN), computed tomography scan BibRef

Liu, L., Dou, Q., Chen, H., Qin, J., Heng, P.,
Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis,
MedImg(39), No. 3, March 2020, pp. 718-728.
IEEE DOI 2004
Lung nodule, benign-malignant diagnosis, attribute score regression, deep learning, multi-task BibRef

Baby, Y.R.[Yadhu Rajan], Sumathy, V.K.R.[Vinod Kumar Ramayyan],
Kernel-based Bayesian clustering of computed tomography images for lung nodule segmentation,
IET-IPR(14), No. 5, 17 April 2020, pp. 890-900.
DOI Link 2004
BibRef

Gupta, A.[Anindya], Saar, T.[Tonis], Martens, O.[Olev], Le Moullec, Y.[Yannick], Sintorn, I.M.[Ida-Maria],
Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks,
IJIST(30), No. 2, 2020, pp. 327-339.
DOI Link 2005
computed tomography, computer-aided detection, false positive reduction, micronodules, silicosis BibRef

Yang, W.[Wei], Xia, W.H.[Wen-Hua], Xie, Y.L.[Yuan-Liang], Mao, S.[Shilong], Li, R.[Rong],
Optimisation analysis of pulmonary nodule diagnostic test based on deep belief nets,
IET-IPR(14), No. 7, 29 May 2020, pp. 1227-1232.
DOI Link 2005
BibRef

Lee, C.H.[Cheng-Hsiung], Jwo, J.S.[Jung-Sing],
Automatic segmentation for pulmonary nodules in CT images based on multifractal analysis,
IET-IPR(14), No. 7, 29 May 2020, pp. 1347-1353.
DOI Link 2005
BibRef

Zheng, J.[Jie], Yang, D.W.[Da-Wei], Zhu, Y.[Yu], Gu, W.H.[Wang-Huan], Zheng, B.B.[Bing-Bing], Bai, C.X.[Chun-Xue], Zhao, L.[Lin], Shi, H.C.[Hong-Cheng], Hu, J.[Jie], Lu, S.H.[Shao-Hua], Shi, W.B.[Wei-Bin], Wang, N.F.[Ning-Fang],
Pulmonary nodule risk classification in adenocarcinoma from CT images using deep CNN with scale transfer module,
IET-IPR(14), No. 8, 19 June 2020, pp. 1481-1489.
DOI Link 2005
BibRef

Tavakoli, M.B.[Mahsa Bank], Orooji, M.[Mahdi], Teimouri, M.[Mehdi], Shahabifar, R.[Ramita],
Segmentation of the pulmonary nodule and the attached vessels in the CT scan of the chest using morphological features and topological skeleton of the nodule,
IET-IPR(14), No. 8, 19 June 2020, pp. 1520-1528.
DOI Link 2005
BibRef

Ni, Z.H.[Zi-Hao], Peng, Y.J.[Yan-Jun],
A serialized classification method for pulmonary nodules based on lightweight cascaded convolutional neural network-long short-term memory,
IJIST(30), No. 4, 2020, pp. 950-962.
DOI Link 2011
convolutional neural networks, false positive reduction, long short-term memory, lung cancer, pulmonary nodule classification BibRef

Rani, K.V.[K. Vijila], Jawhar, S.J.[S. Joseph],
Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification,
IJIST(30), No. 4, 2020, pp. 899-915.
DOI Link 2011
artificial neural network, ATMSBR segmentation, bag of visual words classifier, nanoimaging BibRef

Rani, K.V.[K. Vijila], Jawhar, S.J.[S. Joseph],
Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques,
IET-IPR(14), No. 14, December 2020, pp. 3355-3365.
DOI Link 2012
BibRef

Kumar, S.P.[S. Pramod], Latte, M.V.[Mrityunjaya V.], Siri, S.K.[Sangeeta K.],
Volumetric lung nodule segmentation in thoracic CT scan using freehand sketch,
IET-IPR(14), No. 14, December 2020, pp. 3456-3462.
DOI Link 2012
BibRef

Liu, S., Setio, A.A.A., Ghesu, F.C., Gibson, E., Grbic, S., Georgescu, B., Comaniciu, D.,
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting With Adversarial Attacks,
MedImg(40), No. 1, January 2021, pp. 335-345.
IEEE DOI 2012
Training, Robustness, Lung, Cancer, Computed tomography, Biomedical imaging, Benchmark testing, Lung nodule detection, deep learning BibRef

Jiang, H.L.[Han-Liang], Shen, F.[Fuhao], Gao, F.[Fei], Han, W.D.[Wei-Dong],
Learning efficient, explainable and discriminative representations for pulmonary nodules classification,
PR(113), 2021, pp. 107825.
Elsevier DOI 2103
Pulmonary nodule classification, Convolutional neural network, Neural architecture search, Computer-aided diagnoses, Convolutional block attention module BibRef

Bu, T.[Tian], Yang, Z.Y.[Zhi-Yong], Jiang, S.[Shan], Zhang, G.B.[Guo-Bin], Zhang, H.Y.[Hong-Yun], Wei, L.[Lin],
3D conditional generative adversarial network-based synthetic medical image augmentation for lung nodule detection,
IJIST(31), No. 2, 2021, pp. 670-681.
DOI Link 2105
3D squeeze-and-excitation network, computer-aided detection, conditional generative adversarial network, lung nodules BibRef

Afshar, P.[Parnian], Naderkhani, F.[Farnoosh], Oikonomou, A.[Anastasia], Rafiee, M.J.[Moezedin Javad], Mohammadi, A.[Arash], Plataniotis, K.N.[Konstantinos N.],
MIXCAPS: A capsule network-based mixture of experts for lung nodule malignancy prediction,
PR(116), 2021, pp. 107942.
Elsevier DOI 2106
Tumor type classification, Capsule network, Mixture of experts BibRef

Hernández-Solis, V.[Vicente], Téllez-Velázquez, A.[Arturo], Orantes-Molina, A.[Antonio], Cruz-Barbosa, R.[Raúl],
Lung-Nodule Segmentation Using a Convolutional Neural Network with the U-Net Architecture,
MCPR21(335-344).
Springer DOI 2108
BibRef

Sahu, S.P.[Satya Prakash], Londhe, N.D.[Narendra D.], Verma, S.[Shrish], Singh, B.K.[Bikesh K.], Banchhor, S.K.[Sumit Kumar],
Improved pulmonary lung nodules risk stratification in computed tomography images by fusing shape and texture features in a machine-learning paradigm,
IJIST(31), No. 3, 2021, pp. 1503-1518.
DOI Link 2108
computed tomography, computer-aided diagnosis system, feature extraction, feature selection, nodule segmentation, support vector machine BibRef

Khomkham, B.[Banphatree], Lipikorn, R.[Rajalida],
Pulmonary lesion classification from endobronchial ultrasonography images using adaptive weighted-sum of the upper and lower triangular gray-level co-occurrence matrix,
IJIST(31), No. 3, 2021, pp. 1280-1293.
DOI Link 2108
adaptive weighted-sum of the lower triangular gray-level co-occurrence matrix, support vector machine BibRef

Wang, Q.L.[Qiu-Li], Zhang, X.H.[Xiao-Hong], Zhang, W.[Wei], Gao, M.C.[Ming-Chen], Huang, S.[Sheng], Wang, J.[Jian], Zhang, J.Q.[Jiu-Quan], Yang, D.[Dan], Liu, C.[Chen],
Realistic Lung Nodule Synthesis With Multi-Target Co-Guided Adversarial Mechanism,
MedImg(40), No. 9, September 2021, pp. 2343-2353.
IEEE DOI 2109
BibRef
And: Erratum: MedImg(40), No. 12, December 2021, pp. 3969-3969.
IEEE DOI 2112
Lung, Semantics, Shape, Computed tomography, Generative adversarial networks, Biomedical imaging, adversarial learning BibRef

Mobiny, A.[Aryan], Yuan, P.Y.[Peng-Yu], Cicalese, P.A.[Pietro A.], Moulik, S.K.[Supratik K.], Garg, N.[Naveen], Wu, C.C.[Carol C.], Wong, K.[Kelvin], Wong, S.T.[Stephen T.], He, T.C.[Tian Cheng], Nguyen, H.V.[Hien V.],
Memory-Augmented Capsule Network for Adaptable Lung Nodule Classification,
MedImg(40), No. 10, October 2021, pp. 2869-2879.
IEEE DOI 2110
Lung, Training, Task analysis, Feature extraction, Adaptation models, Data models, Computed tomography, Capsule network, meta-learning BibRef

Balagurunathan, Y.[Yoganand], Beers, A.[Andrew], Mcnitt-Gray, M.[Michael], Hadjiiski, L.[Lubomir], Napel, S.[Sandy], Goldgof, D.[Dmitry], Perez, G.[Gustavo], Arbelaez, P.[Pablo], Mehrtash, A.[Alireza], Kapur, T.[Tina], Yang, E.[Ehwa], Moon, J.W.[Jung Won], Perez, G.B.[Gabriel Bernardino], Delgado-Gonzalo, R.[Ricard], Farhangi, M.M.[M. Mehdi], Amini, A.A.[Amir A.], Ni, R.[Renkun], Feng, X.[Xue], Bagari, A.[Aditya], Vaidhya, K.[Kiran], Veasey, B.[Benjamin], Safta, W.[Wiem], Frigui, H.[Hichem], Enguehard, J.[Joseph], Gholipour, A.[Ali], Castillo, L.S.[Laura Silvana], Daza, L.A.[Laura Alexandra], Pinsky, P.[Paul], Kalpathy-Cramer, J.[Jayashree], Farahani, K.[Keyvan],
Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge,
MedImg(40), No. 12, December 2021, pp. 3748-3761.
IEEE DOI 2112
Training, Lung, Deep learning, Lung cancer, Computed tomography, Biomedical imaging, Pathology, Lung cancer, nodules challenge, deep learning methods in lung CT BibRef

Al-Shabi, M.[Mundher], Shak, K.[Kelvin], Tan, M.[Maxine],
ProCAN: Progressive growing channel attentive non-local network for lung nodule classification,
PR(122), 2022, pp. 108309.
Elsevier DOI 2112
Self-Attention, Non-local network, Nodule classification, Curriculum learning, Deep learning BibRef

Chen, H.[Hao], Xia, Y.[Yu], Duan, H.[Hongbai], Ban, D.[Duo], Yang, Q.[Qing], Qiang, Y.Q.[Yong-Qian],
A computed tomography signs quantization analysis method for pulmonary nodules malignancy grading,
IJIST(31), No. 4, 2021, pp. 2283-2294.
DOI Link 2112
CT signs, evolutionary ensemble learning, grade of malignancy, quantization analysis BibRef

Zhang, X.F.[Xiao-Fang], Li, S.[Suxiao], Zhang, B.[Bin], Dong, J.[Jie], Zhao, S.J.[Shu-Jun], Liu, X.M.[Xiao-Min],
Automatic detection and segmentation of lung nodules in different locations from CT images based on adaptive a-hull algorithm and DenseNet convolutional network,
IJIST(31), No. 4, 2021, pp. 1882-1893.
DOI Link 2112
contour correction, CT image, DenseNet, lung nodules, segmentation, a-hull BibRef

Fu, Y.[Yu], Xue, P.[Peng], Zhao, P.[Peng], Li, N.[Ning], Xu, Z.[Zhuodong], Ji, H.[Huizhong], Zhang, Z.[Zhili], Cui, W.T.[Wen-Tao], Dong, E.[Enqing],
3D multi-resolution deep learning model for diagnosis of multiple pathological types on pulmonary nodules,
IJIST(32), No. 1, 2022, pp. 74-87.
DOI Link 2201
deep learning, multiple pathological types, multi-resolution method, pulmonary nodules BibRef

Dodia, S.[Shubham], Basava, A.[Annappa], Anand, M.P.[Mahesh Padukudru],
A novel receptive field-regularized V-net and nodule classification network for lung nodule detection,
IJIST(32), No. 1, 2022, pp. 88-101.
DOI Link 2201
lung cancer, NCNet, nodule detection, pseudo-color, RFR V-net BibRef

Wang, G.[Guotai], Zhai, S.[Shuwei], Lasio, G.[Giovanni], Zhang, B.[Baoshe], Yi, B.[Byong], Chen, S.F.[Shi-Feng], Macvittie, T.J.[Thomas J.], Metaxas, D.N.[Dimitris N.], Zhou, J.H.[Jing-Hao], Zhang, S.T.[Shao-Ting],
Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis From Lung CT Scans With Multi-Scale Guided Dense Attention,
MedImg(41), No. 3, March 2022, pp. 531-542.
IEEE DOI 2203
Image segmentation, Lesions, Lung, Computed tomography, Task analysis, Lung cancer, Semi-supervised learning, lung CT BibRef

Fu, X.H.[Xiao-Hang], Bi, L.[Lei], Kumar, A.[Ashnil], Fulham, M.[Michael], Kim, J.M.[Jin-Man],
An attention-enhanced cross-task network to analyse lung nodule attributes in CT images,
PR(126), 2022, pp. 108576.
Elsevier DOI 2204
Deep learning, Lung nodule analysis, Multi-task, Computed tomography (CT), Attention BibRef

Momoki, Y.[Yohei], Ichinose, A.[Akimichi], Shigeto, Y.[Yutaro], Honda, U.[Ukyo], Nakamura, K.[Keigo], Matsumoto, Y.J.[Yu-Ji],
Characterization of Pulmonary Nodules in Computed Tomography Images Based on Pseudo-Labeling Using Radiology Reports,
CirSysVideo(32), No. 5, May 2022, pp. 2582-2591.
IEEE DOI 2205
Radiology, Lung, Annotations, Training data, Manuals, Computed tomography, Training, Computer-aided diagnosis, radiology report BibRef

Zhang, X.F.[Xiao-Fang], Zhang, B.[Bin], Liu, X.M.[Xiao-Min], Dong, J.[Jie], Zhao, S.J.[Shu-Jun], Li, S.[Suxiao],
Accurate classification of nodules and non-nodules from computed tomography images based on radiomics and machine learning algorithms,
IJIST(32), No. 3, 2022, pp. 956-968.
DOI Link 2205
classification, CT image, lung nodules, machine learning, radiomics BibRef

Mei, J.[Jie], Cheng, M.M.[Ming-Ming], Xu, G.[Gang], Wan, L.R.[Lan-Ruo], Zhang, H.[Huan],
SANet: A Slice-Aware Network for Pulmonary Nodule Detection,
PAMI(44), No. 8, August 2022, pp. 4374-4387.
IEEE DOI 2207
Lung, Computed tomography, Feature extraction, Proposals, Object detection, Pulmonary nodule detection, nodule dataset, false positive reduction BibRef

Liao, Z.H.[Ze-Hui], Xie, Y.T.[Yu-Tong], Hu, S.S.[Shi-Shuai], Xia, Y.[Yong],
Learning From Ambiguous Labels for Lung Nodule Malignancy Prediction,
MedImg(41), No. 7, July 2022, pp. 1874-1884.
IEEE DOI 2207
Lung, Reliability, Annotations, Cancer, Computed tomography, Predictive models, Training, Nodule malignancy prediction, computed tomography BibRef

Zhang, Z.[Zhili], Xiao, T.[Taohui], Fu, Y.[Yu], Gao, Y.Q.[Yu-Qiang], Ren, M.[Meirong], Cui, W.T.[Wen-Tao], Dong, E.[Enqing],
3D multi-resolution attention capsule network for diagnosing multi-pathological types of pulmonary nodules,
IJIST(32), No. 5, 2022, pp. 1727-1742.
DOI Link 2209
attention mechanism, capsule network, deep learning, multi-pathological classification, pulmonary nodule diagnosis BibRef

Jeniba, J.S.[J. Sahaya], Milton, A.,
A multilevel self-attention based segmentation and classification technique using Directional Hexagonal Mixed Pattern algorithm for lung nodule detection in thoracic CT image,
IJIST(32), No. 5, 2022, pp. 1496-1510.
DOI Link 2209
classification, Directional Hexagonal Mixed Pattern, lung nodules, multilevel self-attention and CT images, segmentation BibRef

Yi, L.[Le], Zhang, L.[Lei], Xu, X.Y.[Xiu-Yuan], Guo, J.X.[Ji-Xiang],
Multi-Label Softmax Networks for Pulmonary Nodule Classification Using Unbalanced and Dependent Categories,
MedImg(42), No. 1, January 2023, pp. 317-328.
IEEE DOI 2301
Lung, Feature extraction, Task analysis, Morphology, Computed tomography, Indexes, Lung cancer, Category imbalance, multi-label classification BibRef

Liu, W.H.[Wei-Hua], Liu, X.B.[Xia-Bi], Luo, X.B.[Xiong-Biao], Wang, M.R.[Mu-Rong], Han, G.H.[Guang-Hui], Zhao, X.M.[Xin-Ming], Zhu, Z.[Zheng],
A pyramid input augmented multi-scale CNN for GGO detection in 3D lung CT images,
PR(136), 2023, pp. 109261.
Elsevier DOI 2301
Ground-Glass Opacity nodules. GGO detection, Multi-scale processing, 3D CT scans, Pyramid inputs BibRef

Tao, J.[Juliang], Wang, Y.L.[Yong-Li], Ding, X.Y.[Xiao-Yun],
A quantitative evaluation of lung nodule spiculation based on image enhancement,
IET-IPR(17), No. 4, 2023, pp. 1086-1096.
DOI Link 2303
image enhancement, lung nodule segmentation, Random Walk, spiculation BibRef

Wang, T.[Tong], Wu, F.[Fubin], Lu, H.R.[Hao-Ran], Xu, S.Z.[Sheng-Zhou],
CA-UNet: Convolution and attention fusion for lung nodule segmentation,
IJIST(33), No. 5, 2023, pp. 1469-1479.
DOI Link 2310
channel attention module, lung nodule, segmentation, Swin Transformer block, U-Net BibRef

Qiu, J.R.[Jun-Rong], Li, B.[Bin], Liao, R.Q.[Ri-Qiang], Mo, H.Q.[Hong-Qiang], Tian, L.F.[Lian-Fang],
A dual-task region-boundary aware neural network for accurate pulmonary nodule segmentation,
JVCIR(96), 2023, pp. 103909.
Elsevier DOI 2310
Pulmonary nodules, Segmentation, Region-boundary joint learning, Multi-task learning BibRef

Fang, J.S.[Jian-Sheng], Wang, J.W.[Jing-Wen], Li, A.[Anwei], Yan, Y.G.[Yu-Guang], Liu, H.B.[Hong-Bo], Li, J.J.[Jia-Jian], Yang, H.F.[Hui-Fang], Hou, Y.H.[Yong-He], Yang, X.N.[Xue-Ning], Yang, M.[Ming], Liu, J.[Jiang],
Parameterized Gompertz-Guided Morphological AutoEncoder for Predicting Pulmonary Nodule Growth,
MedImg(42), No. 12, December 2023, pp. 3602-3613.
IEEE DOI 2312
BibRef

Selvadass, S.[Salomi], Bruntha, P.M.[P. Malin], Sagayam, K.M.[K. Martin], Günerhan, H.[Hat?ra],
SAtUNet: Series atrous convolution enhanced U-Net for lung nodule segmentation,
IJIST(34), No. 1, 2024, pp. e22964.
DOI Link 2401
computed tomography, computer-aided detection, dilated convolution, lung nodule segmentation, U-Net BibRef

Demirel, M.[Mehmet], Mills, B.[Bethany], Gaughan, E.[Erin], Dhaliwal, K.[Kevin], Hopgood, J.R.[James R.],
Bayesian Statistical Analysis for Bacterial Detection in Pulmonary Endomicroscopic Fluorescence Lifetime Imaging,
IP(33), 2024, pp. 1241-1256.
IEEE DOI 2402
Microorganisms, Lung, Fluorescence, Imaging, Optical imaging, Endomicroscopy, Biomedical optical imaging, Bacteria detection, Bayesian statistical analysis BibRef


Zhu, Q.[Qikui], Wang, Y.Q.[Yan-Qing], Chu, X.P.[Xiang-Peng], Yang, X.W.[Xiong-Wen], Zhong, W.Z.[Wen-Zhao],
Multi-view Coupled Self-attention Network for Pulmonary Nodules Classification,
ACCV22(VI:37-51).
Springer DOI 2307
BibRef

Lucas, M.[Mirtha], Lerma, M.[Miguel], Furst, J.[Jacob], Raicu, D.[Daniela],
Explainable Model for Localization of Spiculation in Lung Nodules,
MIA-COVID19D22(457-471).
Springer DOI 2304
BibRef

Wang, Y.Y.[Yi-Yang], Qiu, B.[Bowen], Ramaraj, T.[Thiruvarangan], Ustun, I.[Ilyas], Furst, J.[Jacob], Raicu, D.[Daniela],
Lung Nodule Malignancy Subtype Discovery with Semantic Learning,
ICPR22(4234-4240)
IEEE DOI 2212
Solid modeling, Design automation, Annotations, Image databases, Semantics, Lung, Lung cancer BibRef

Fu, X.L.[Xin-Liang], Zheng, J.Y.[Jia-Yin], Mai, J.[Juanyun], Shao, Y.B.[Yan-Bo], Wang, M.H.[Ming-Hao], Li, L.[Linyu], Diao, Z.Q.[Zhao-Qi], Chen, Y.L.[Yu-Long], Xiao, J.Y.[Jian-Yu], You, J.[Jian], Yin, A.[Airu], Yang, Y.[Yang], Qiu, X.C.[Xiang-Cheng], Tao, J.S.[Jin-Sheng], Wang, B.[Bo], Ji, H.[Hua],
A Coarse-to-Fine Morphological Approach with Knowledge-Based Rules and Self-Adapting Correction for Lung Nodules Segmentation,
ICIP22(1696-1700)
IEEE DOI 2211
Image segmentation, Solid modeling, Thresholding (Imaging), Automation, Biological system modeling, Knowledge based systems, self-adapting strategy BibRef

Chaudhry, H.A.H.[Hafiza Ayesha Hoor], Renzulli, R.[Riccardo], Perlo, D.[Daniele], Santinelli, F.[Francesca], Tibaldi, S.[Stefano], Cristiano, C.[Carmen], Grosso, M.[Marco], Fiandrotti, A.[Attilio], Lucenteforte, M.[Maurizio], Cavagnino, D.[Davide],
Lung Nodules Segmentation with DeepHealth Toolkit,
DeepHealth22(487-497).
Springer DOI 2208
BibRef

Pattisapu, V.K.[Varaha Karthik], Daunhawer, I.[Imant], Weikert, T.[Thomas], Sauter, A.[Alexander], Stieltjes, B.[Bram], Vogt, J.E.[Julia E.],
PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images,
GCPR20(445-458).
Springer DOI 2110
BibRef

Chen, W.[Wei], Wang, Q.[Qiuli], Wang, K.[Kun], Yang, D.[Dan], Zhang, X.H.[Xiao-Hong], Liu, C.[Chen], Li, Y.C.[Yu-Cong],
MTGAN: Mask and Texture-driven Generative Adversarial Network for Lung Nodule Segmentation,
ICPR21(1029-1035)
IEEE DOI 2105
Training, Visualization, Shape, Computed tomography, Lung, Lung cancer, Benchmark testing BibRef

Chen, W.[Wei], Wang, Q.[Qiuli], Yang, D.[Dan], Zhang, X.H.[Xiao-Hong], Liu, C.[Chen], Li, Y.C.[Yu-Cong],
End-to-End Multi-Task Learning for Lung Nodule Segmentation and Diagnosis,
ICPR21(6710-6717)
IEEE DOI 2105
Deep learning, Design automation, Lung, Feature extraction, Pattern recognition, Computer aided diagnosis, Task analysis, Classification BibRef

Olulana, K.[Kolawole], Owolawi, P.[Pius], Tu, C.L.[Chun-Ling], Abe, B.[Bolanle],
Nodule Generation of Lung CT Images Using a 3d Convolutional LSTM Network,
ISVC20(II:753-760).
Springer DOI 2103
BibRef

Shaffie, A., Soliman, A., Khalifeh, H.A., Ghazal, M., Taher, F., Elmaghraby, A., Keynton, R., El-Baz, A.,
A Comprehensive Framework For Accurate Classification of Pulmonary Nodules,
ICIP20(408-412)
IEEE DOI 2011
Lung, Cancer, Feature extraction, Computed tomography, Histograms, Isosurfaces, CAD, SIHOG, Deep Neural Network. BibRef

Xu, W., Wang, K., Lin, J., Lu, Y., Huang, S., Zhang, X.,
Knowledge-Guided And Hyper-Attention Aware Joint Network For Benign-Malignant Lung Nodule Classification,
ICIP20(310-314)
IEEE DOI 2011
Cancer, Feature extraction, Lung, Semantics, Computed tomography, Visualization, Training, Lung nodule classification, Priordomain knowledge BibRef

Mejía, C.I.L.[Cecilia Irene Loeza], Biswal, R.R., Rodriguez-Tello, E.[Eduardo], Ochoa-Ruiz, G.[Gilberto],
Accurate Identification of Tomograms of Lung Nodules Using Cnn: Influence of the Optimizer, Preprocessing and Segmentation,
MCPR20(242-250).
Springer DOI 2007
BibRef

Kaluva, K.C.[Krishna Chaitanya], Vaidhya, K.[Kiran], Chunduru, A.[Abhijith], Tarai, S.[Sambit], Nadimpalli, S.P.P.[Sai Prasad Pranav], Vaidya, S.[Suthirth],
An Automated Workflow for Lung Nodule Follow-up Recommendation Using Deep Learning,
ICIAR20(II:369-377).
Springer DOI 2007
BibRef

Katz, O.[Or], Presil, D.[Dan], Cohen, L.[Liz], Schwartzbard, Y.[Yael], Hoch, S.[Sarah], Kashani, S.[Shlomo],
Pulmonary-nodule Detection Using an Ensemble of 3d Se-resnet18 and Dpn68 Models,
ICIAR20(II:378-385).
Springer DOI 2007
BibRef

Tian, Z.H.[Zhen-Huan], Jia, Y.Z.[Yi-Zhuan], Men, X.J.[Xue-Jun], Sun, Z.W.[Zhong-Wei],
3dcnn for Pulmonary Nodule Segmentation and Classification,
ICIAR20(II:386-395).
Springer DOI 2007
BibRef

Galdran, A.[Adrian], Bouchachia, H.[Hamid],
Residual Networks for Pulmonary Nodule Segmentation and Texture Characterization,
ICIAR20(II:396-405).
Springer DOI 2007
BibRef

Atwal, G.[Gurraj], Phoulady, H.A.[Hady Ahmady],
Automatic Lung Cancer Follow-up Recommendation with 3d Deep Learning,
ICIAR20(II:406-418).
Springer DOI 2007
BibRef

Rassadin, A.[Alexandr],
Deep Residual 3d U-net for Joint Segmentation and Texture Classification of Nodules in Lung,
ICIAR20(II:419-427).
Springer DOI 2007
BibRef

Han, C., Kitamura, Y., Kudo, A., Ichinose, A., Rundo, L., Furukawa, Y., Umemoto, K., Li, Y., Nakayama, H.,
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-Based CT Image Augmentation for Object Detection,
3DV19(729-737)
IEEE DOI 1911
Training, Lung, Computed tomography, Biomedical imaging, Generative adversarial networks, 3D Lung Nodule Detection BibRef

Shaffie, A., Soliman, A., Khalifeh, H.A., Taher, F., Ghazal, M., Dunlap, N., Elmaghraby, A., Keynton, R., El-Baz, A.,
A Novel CT-Based Descriptors for Precise Diagnosis of Pulmonary Nodules,
ICIP19(1400-1404)
IEEE DOI 1910
Computer Aided Diagnosis, RALBP, MGRF, Computer Tomography, Spherical Harmonics, Autoencoder BibRef

Zhang, G., Luo, Y., Zhu, D., Xu, Y., Sun, Y., Lu, J.,
Spatial Pyramid Dilated Network for Pulmonary Nodule Malignancy Classification,
ICPR18(3911-3916)
IEEE DOI 1812
Convolution, Feature extraction, Cancer, Computed tomography, Neural networks, Kernel BibRef

Veduruparthi, B.K., Mukhopadhyay, J., Das, P.P., Saha, M., Prasath, S., Ray, S., Shrimali, R.K., Chatterjee, S.,
Segmentation of Lung Tumor in Cone Beam CT Images Based on Level-Sets,
ICIP18(1398-1402)
IEEE DOI 1809
Tumors, Image segmentation, Computed tomography, Image edge detection, Light rail systems, Local Rank Transform BibRef

Zhu, W., Liu, C., Fan, W., Xie, X.,
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification,
WACV18(673-681)
IEEE DOI 1806
cancer, computerised tomography, feature extraction, feedforward neural nets, image classification, image coding, BibRef

Snezhko, E.V., Kharuzhyk, S.A., Tuzikov, A.V., Kovalev, V.A.,
Small Nodules Localization on CT Images of Lungs,
PTVSBB17(141-144).
DOI Link 1805
BibRef

Zhu, Y., Xu, Y., Ni, B., Zhang, J., Yang, X.,
Enhancing pulmonary nodule detection via cross-modal alignment,
VCIP17(1-4)
IEEE DOI 1804
computerised tomography, feature extraction, image classification, lung, medical image processing, Training BibRef

Shaffie, A., Soliman, A., Ghazal, M., Taher, F., Dunlap, N., Wang, B., Elmaghraby, A., Gimel'farb, G., El-Baz, A.,
A new framework for incorporating appearance and shape features of lung nodules for precise diagnosis of lung cancer,
ICIP17(1372-1376)
IEEE DOI 1803
Markov processes, cancer, computerised tomography, feature extraction, image classification, image coding, Spherical Harmonics BibRef

Xie, Y.T.[Yu-Tong], Zhang, J.P.[Jian-Peng], Liu, S.[Sidong], Cai, W.D.[Wei-Dong], Xia, Y.[Yong],
Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features,
MCV16(116-125).
Springer DOI 1711
BibRef

Pawelczyk, K.[Krzysztof], Kawulok, M.[Michal], Nalepa, J.[Jakub], Hayball, M.P.[Michael P.], McQuaid, S.J.[Sarah J.], Prakash, V.[Vineet], Ganeshan, B.[Balaji],
Towards Detecting High-Uptake Lesions from Lung CT Scans Using Deep Learning,
CIAP17(II:310-320).
Springer DOI 1711
BibRef

Papiez, B.W.[Bartlomiej W.], Brady, S.M.[Sir Michael], Schnabel, J.A.[Julia A.],
Mass Transportation for Deformable Image Registration with Application to Lung CT,
RAMBO17(66-74).
Springer DOI 1711
BibRef

Luo, Z., Brubaker, M.A.[Marcus A.], Brudno, M.,
Size and Texture-Based Classification of Lung Tumors with 3D CNNs,
WACV17(806-814)
IEEE DOI 1609
Biological neural networks, Cancer, Computed tomography, Lungs, Tumors BibRef

Yan, X.J.[Xing-Jian], Pang, J.N.[Jia-Ning], Qi, H.[Hang], Zhu, Y.X.[Yi-Xin], Bai, C.X.[Chun-Xue], Geng, X.[Xin], Liu, M.[Mina], Terzopoulos, D.[Demetri], Ding, X.W.[Xiao-Wei],
Classification of Lung Nodule Malignancy Risk on Computed Tomography Images Using Convolutional Neural Network: A Comparison Between 2D and 3D Strategies,
MCBMIIA16(III: 91-101).
Springer DOI 1704
BibRef

Bobadilla, J.C.M.[Julio Cesar Mendoza], Pedrini, H.[Helio],
Lung Nodule Classification Based on Deep Convolutional Neural Networks,
CIARP16(117-124).
Springer DOI 1703
BibRef

Ishihara, M.[Masaki], Matsuda, Y.J.[Yu-Ji], Sugimura, M.[Masahiko], Endo, S.[Susumu], Takebe, H.[Hiroaki], Baba, T.[Takayuki], Uehara, Y.[Yusuke],
An Image Registration Method with Radial Feature Points Sampling: Application to Follow-Up CT Scans of a Solitary Pulmonary Nodule,
PSIVT15(512-525).
Springer DOI 1602
BibRef

Novo, J.[Jorge], Goncalves, L.[Luis], Mendonca, A.M.[Ana Maria], Campilho, A.[Aurelio],
3D lung nodule candidate detection in multiple scales,
MVA15(61-64)
IEEE DOI 1507
Cancer BibRef

Kumar, D.[Devinder], Wong, A.[Alexander], Clausi, D.A.[David A.],
Lung Nodule Classification Using Deep Features in CT Images,
CRV15(133-138)
IEEE DOI 1507
Accuracy BibRef

Duggan, N.[Nóirín], Bae, E.[Egil], Shen, S.[Shiwen], Hsu, W.[William], Bui, A.[Alex], Jones, E.[Edward], Glavin, M.[Martin], Vese, L.[Luminita],
A Technique for Lung Nodule Candidate Detection in CT Using Global Minimization Methods,
EMMCVPR15(478-491).
Springer DOI 1504
BibRef

Liu, Y.[Yang], Wang, Z.Q.[Zhong-Qiu], Guo, M.[Maozu], Li, P.[Ping],
Hidden conditional random field for lung nodule detection,
ICIP14(3518-3521)
IEEE DOI 1502
Biomedical imaging BibRef

Forsberg, D.[Daniel], Monsef, N.[Nastaran],
Evaluating Cell Nuclei Segmentation for Use on Whole-Slide Images in Lung Cytology,
ICPR14(3380-3385)
IEEE DOI 1412
BibRef

Kaya, A.[Aydln], Can, A.B.[Ahmet Burak],
eFis: A Fuzzy Inference Method for Predicting Malignancy of Small Pulmonary Nodules,
ICIAR14(II: 255-262).
Springer DOI 1410
BibRef

Gonçalves, L.[Luis], Novo, J.[Jorge], Campilho, A.[Aurélio],
Central Medialness Adaptive Strategy for 3D Lung Nodule Segmentation in Thoracic CT Images,
ICIAR16(583-590).
Springer DOI 1608
BibRef

Novo, J., Rouco, J., Mendonça, A., Campilho, A.[Aurélio],
Reliable Lung Segmentation Methodology by Including Juxtapleural Nodules,
ICIAR14(II: 227-235).
Springer DOI 1410
BibRef

Papiez, B.W.[Bartlomiej W.], Tapmeier, T.[Thomas], Heinrich, M.P.[Mattias P.], Muschel, R.J.[Ruth J.], Schnabel, J.A.[Julia A.],
Motion Correction of Intravital Microscopy of Preclinical Lung Tumour Imaging Using Multichannel Structural Image Descriptor,
WBIR14(164-173).
Springer DOI 1407
BibRef

Lam, M., Doppa, J.R., Hu, X.[Xu], Todorovic, S., Dietterich, T., Reft, A., Daly, M.,
Learning to Detect Basal Tubules of Nematocysts in SEM Images,
AccBio13(190-196)
IEEE DOI 1403
biology computing BibRef

Li, Y.[Yang], Wen, D.[Dunwei], Wang, K.[Ke], Hou, A.[A'lin],
Mixed Kernel Function SVM for Pulmonary Nodule Recognition,
CIAP13(II:449-458).
Springer DOI 1309
BibRef

Aggarwal, P.[Preeti], Sardana, H.K., Vig, R.[Renu],
Correlation between Biopsy Confirmed Cases and Radiologist's Annotations in the Detection of Lung Nodules by Expanding the Diagnostic Database Using Content Based Image Retrieval,
CAIP13(531-538).
Springer DOI 1308
BibRef

Vinay, K., Rao, A., Kumar, G.H.,
Computerized Analysis of Classification of Lung Nodules and Comparison between Homogeneous and Heterogeneous Ensemble of Classifier Model,
NCVPRIPG11(231-234).
IEEE DOI 1205
BibRef

Acharya, M.[Mekhala], Kinser, J.[Jason], Nathan, S.[Steven], Albano, M.C.[Marcia C.], Schlegel, L.[Lori],
An image analysis method for quantification of idiopathic pulmonary fibrosis,
AIPR11(1-5).
IEEE DOI 1204
BibRef

Farag, A.A.[Amal A.], Abd el Munim, H.E.[Hossam E.], Graham, J.[James], Farag, A.A.[Aly A.], Elshazly, S.[Salwa], El-Mogy, S.[Sabry], El-Mogy, M.[Mohamed], Falk, R.[Robert], Al-Jafary, S.[Sahar], Mahdi, H.[Hani], Milam, R.[Rebecca],
Variational approach for segmentation of lung nodules,
ICIP11(2157-2160).
IEEE DOI 1201
BibRef

Zinoveva, O.[Olga], Zinovev, D.[Dmitriy], Siena, S.A.[Stephen A.], Raicu, D.S.[Daniela S.], Furst, J.[Jacob], Armato, S.G.[Samuel G.],
A Texture-Based Probabilistic Approach for Lung Nodule Segmentation,
ICIAR11(II: 21-30).
Springer DOI 1106
BibRef

Farag, A.[Amal], Ali, A.M.[Asem M.], Graham, J.[James], Elhabian, S.Y.[Shireen Y.], Farag, A.A.[Aly A.], Falk, R.[Robert],
Feature-Based Lung Nodule Classification,
ISVC10(III: 79-88).
Springer DOI 1011
BibRef

Choi, W.J.[Wook-Jin], Choi, T.S.[Tae-Sun],
Computer-aided detection of pulmonary nodules using genetic programming,
ICIP10(4353-4356).
IEEE DOI 1009
BibRef

Farag, A.A.[Amal A.], Graham, J.[James], Elshazly, S.[Salwa], Farag, A.A.[Aly A.],
Statistical modeling of the lung nodules in low dose computed tomography scans of the chest,
ICIP10(4281-4284).
IEEE DOI 1009
BibRef

Farag, A.A.[Amal A.], Graham, J.[James], Elshazly, S.[Salwa], Farag, A.A.[Aly A.],
Data-Driven Lung Nodule Models for Robust Nodule Detection in Chest CT,
ICPR10(2588-2591).
IEEE DOI 1008
BibRef

Farag, A.A.[Amal A.], Graham, J.[James], Farag, A.A.[Aly A.], Falk, R.[Robert],
Lung Nodule Modeling: A Data-Driven Approach,
ISVC09(I: 347-356).
Springer DOI 0911
BibRef

Tolouee, A., Abrishami-Moghaddam, H., Garnavi, R., Forouzanfar, M., Giti, M.,
Texture Analysis in Lung HRCT Images,
DICTA08(305-311).
IEEE DOI 0812
BibRef

Shi, Z.H.[Zheng-Hao], Bai, J.[Jun], He, L.F.[Li-Feng], Nakamura, T., Yao, Q.Z.[Quan-Zhu], Itoh, H.,
A Method for Enhancing Lung Nodules in Chest Radiographs by Use of LoG Filter,
CISP09(1-4).
IEEE DOI 0910
BibRef

Zheng, Y.J.[Yuan-Jie], Kambhamettu, C.[Chandra], Bauer, T.[Thomas], Steiner, K.[Karl],
Accurate estimation of pulmonary nodule's growth rate in CT images with nonrigid registration and precise nodule detection and segmentation,
MMBIA09(101-108).
IEEE DOI 0906
BibRef

Wei, E.[Erling], Yan, J.[Jiayong], Xu, M.[Mantao], Zhang, J.W.[Ji-Wu],
A novel segmentation algorithm for pulmonary nodule in chest radiograph,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Suzuki, K.[Kenji], Shi, Z.H.[Zheng-Hao], Zhang, J.[Jun],
Supervised enhancement of lung nodules by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD),
ICPR08(1-4).
IEEE DOI 0812
BibRef

Bauer, C.[Christian], Bischof, H.[Horst],
A Novel Approach for Detection of Tubular Objects and Its Application to Medical Image Analysis,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Zheng, Y.J.[Yuan-Jie], Steiner, K.[Karl], Bauer, T.[Thomas], Yu, J.Y.[Jing-Yi], Shen, D.G.[Ding-Gang], Kambhamettu, C.[Chandra],
Lung Nodule Growth Analysis from 3D CT Data with a Coupled Segmentation and Registration Framework,
MMBIA07(1-8).
IEEE DOI 0710
BibRef

Antonelli, M.[Michela], Yang, G.Z.[Guang-Zhong],
Lung Nodule Detection using Eye-Tracking,
ICIP07(II: 457-460).
IEEE DOI 0709
BibRef

Memarian, N., Alirezaie, J., Babyn, P.,
Computerized Detection of Lung Nodules with an Enhanced False Positive Reduction Scheme,
ICIP06(1921-1924).
IEEE DOI 0610
BibRef

Sousa, A.V.[António V.], Mendonça, A.M.[Ana Maria], Campilho, A.[Aurélio],
Chromatographic Pattern Recognition Using Optimized One-Class Classifiers,
IbPRIA09(449-456).
Springer DOI 0906
BibRef
Earlier:
Minimizing the Imbalance Problem in Chromatographic Profile Classification with One-Class Classifiers,
ICIAR08(xx-yy).
Springer DOI 0806
BibRef
Earlier:
The Class Imbalance Problem in TLC Image Classification,
ICIAR06(II: 513-523).
Springer DOI 0610
BibRef

Pereira, C.S.[Carlos S.], Fernandes, H.[Hugo], Mendonça, A.M.[Ana Maria], Campilho, A.[Aurélio],
Detection of Lung Nodule Candidates in Chest Radiographs,
IbPRIA07(II: 170-177).
Springer DOI 0706
BibRef

Pereira, C.S.[Carlos S.], Mendonça, A.M.[Ana Maria], Campilho, A.[Aurélio],
Evaluation of Contrast Enhancement Filters for Lung Nodule Detection,
ICIAR07(878-888).
Springer DOI 0708
BibRef

Pereira, C.S.[Carlos S.], Alexandre, L.A.[Luís A.], Mendonça, A.M.[Ana Maria], Campilho, A.[Aurélio],
A Multiclassifier Approach for Lung Nodule Classification,
ICIAR06(II: 612-623).
Springer DOI 0610
BibRef

Kubota, T.[Toshiro], Okada, K.[Kazunori],
Estimating Diameters of Pulmonary Nodules with Competition-Diffusion and Robust Ellipsoid Fit,
CVBIA05(324-334).
Springer DOI 0601
BibRef

Campadelli, P., Casiraghi, E., Valentini, G.,
Lung Nodules Detection and Classification,
ICIP05(I: 1117-1120).
IEEE DOI 0512
BibRef

Corrêa Silva, A.[Aristófanes], Cardoso de Paiva, A.[Anselmo], Carvalho, P.C.P.[Paulo C.P.], Gattass, M.[Marcelo],
Semivariogram and SGLDM Methods Comparison for the Diagnosis of Solitary Lung Nodule,
IbPRIA05(II:479).
Springer DOI 0509
BibRef

Silva, J.S.[José Silvestre], Santos, B.S.[Beatriz Sousa], Silva, A.[Augusto], Madeira, J.[Joaquim],
A Level-Set Based Volumetric CT Segmentation Technique: A Case Study with Pulmonary Air Bubbles,
ICIAR04(II: 68-75).
Springer DOI 0409
BibRef

Nakamura, Y., Fukano, G., Takizawa, H., Mizuno, S., Yamamoto, S., Matsumoto, T., Tateno, Y., Iinuma, T.,
Eigen nodule: view-based recognition of lung nodule in chest X-ray CT images using subspace method,
ICPR04(IV: 681-684).
IEEE DOI 0409
BibRef

Takizawa, H., Yamamoto, S., Matsumoto, T., Tateno, Y., Iinuma, T., Matsumoto, M.,
Recognition of lung nodules from x-ray ct images using 3d markov random field models,
ICPR02(I: 99-102).
IEEE DOI 0211
BibRef

Mousa, W.A.H., Khan, M.A.U.,
Lung nodule classification utilizing support vector machines,
ICIP02(III: 153-156).
IEEE DOI 0210
BibRef

Kawata, Y., Niki, N., Ohrnatsu, H., Kusumato, M., Kakinuma, R., Mori, K., Nishiyama, H., Eguchi, K., Kaneko, M., Moriyama, N.,
Three-dimensional CT image retrieval in a database of pulmonary nodules,
ICIP02(III: 149-152).
IEEE DOI 0210
BibRef

Kawata, Y., Niki, N., Ohmatsu, H., Kakinuma, R., Mori, K., Eguchi, K., Kaneko, M., Moriyama, N.,
Curvature based analysis of internal structure of pulmonary nodules using thin-section CT images,
ICIP98(III: 851-855).
IEEE DOI 9810
BibRef

Minami, K., Kawata, Y., Niki, N., Mori, K., Ohmatsu, H., Kakinuma, R., Eguchi, K., Kusumoto, M., Kaneko, M., Moriyama, N.,
Computerized Characterization of Contrast Enhancement Patterns for Classifying Pulmonary Nodules,
ICIP01(II: 897-900).
IEEE DOI 0108
BibRef
Earlier: Takagi, N., A2, A3 only: ICIP00(Vol I: 188-191).
IEEE DOI 0008
BibRef

Kawata, Y., Niki, N., Ohmatsu, H., Kusumoto, M., Kakinuma, R., Mori, K., Nishiyama, H., Eguchi, K., Kaneko, M., Moriyama, N.,
Computerized Analysis of 3-d Pulmonary Nodule Images in Surrounding and Internal Structure Feature Spaces,
ICIP01(II: 889-892).
IEEE DOI 0108
BibRef

Kubo, M., Kawata, Y., Niki, N., Eguchi, K., Ohmatsu, H., Kakinuma, R., Kaneko, M., Kusumoto, M., Moriyama, N., Mori, K., Nishiyama, H.,
Automatic Extraction of Pulmonary Fissures from Multidetector-row CT Images,
ICIP01(III: 1091-1094).
IEEE DOI 0108
BibRef

Kubota, K., Kubo, M., Kawata, Y., Niki, N., Eguchi, K., Omatsu, H., Kakinuma, R., Kaneko, M., Moriyama, N.,
The Results in the Clinical Trial of CAD System for Lung Cancer Using Helical CT Images,
ICIP01(I: 313-316).
IEEE DOI 0108
BibRef

Kawata, Y., Niki, N.,
Surrounding Structures Analysis of Pulmonary Nodules Using Differential Geometry Based Vector Fields,
ICIP00(Vol III: 424-427).
IEEE DOI 0008
BibRef
And:
Internal Structure Analysis of Pulmonary Nodules in Topological and Histogram Feature Spaces,
ICIP00(Vol I: 168-171).
IEEE DOI 0008
BibRef

Sammouda, M., Niki, N.,
Analysis of Color Images of Tissues Derived from Patients with Adenocarcinoma of the Lung,
ICIP00(Vol I: 192-195).
IEEE DOI 0008
BibRef

Kubo, M., Niki, N., Eguchi, K., Kaneko, M., Kusumoto, M., Moriyama, N., Omatsu, H., Kakinuma, R., Nishiyama, H., Mori, K., Yamaguchi,
Extraction of Pulmonary Fissures from HRCT Images Based on Surface Curvatures Analysis and Morphology Filters,
ICPR00(Vol I: 490-493).
IEEE DOI 0009
BibRef

Yamamoto, T., Ukai, Y., Kubo, M., Niki, N.,
Computer Aided Diagnosis System with Functions to Assist Comparative Reading for Lung Cancer Based on Helical CT Image,
ICIP00(Vol I: 180-183).
IEEE DOI 0008
BibRef

Kubo, M., Niki, N.,
Extraction of Pulmonary Fissures from Thin-section CT Images Using Calculation of Surface-curvatures and Morphology Filters,
ICIP00(Vol II: 637-640).
IEEE DOI 0008
BibRef

Kubo, M., Tozaki, T., Niki, N., Nakagawa, S., Yamaguchi, N., Eguchi, K., Kaneko, M., Omatsu, H., and Moriyama, N.,
Bias Field Correction of Chest Thin Section CT Images,
ICIP97(III: 551-554).
IEEE DOI BibRef 9700

Kawata, Y., Niki, N., Ohmatsu, H., Kusumoto, M., Kakinuma, R., Mori, K., Nishiyama, H., Eguchi, K., Kaneko, M., Moriyama, N.,
Computerized Analysis of Pulmonary Nodules in Topological and Histogram Feature Spaces,
ICPR00(Vol IV: 332-335).
IEEE DOI 0009
BibRef

Kawata, Y., Niki, N., Ohmatsu, H., Kusumoto, M., Kakinuma, R., Mori, K., Nishiyama, H., Eguchi, K., Kaneko, M., Moriyama, N.,
Computer aided differential diagnosis of pulmonary nodules using curvature based analysis,
CIAP99(470-475).
IEEE DOI 9909
BibRef

Ohmatsu, H.[Hironobu], Kawata, Y.[Yoshiki], Niki, N.[Noboru], Kaneko, M., Satoh, H.[Hitoshi], Kanazawa, K., Eguchi, K.[Kenji], Moriyama, N.[Noriyuki], Kakinuma, Y.,
Computer-Aided Diagnosis for Pulmonary Nodules Based on Helical CT Images,
ICPR98(Vol II: 1683-1685).
IEEE DOI 9808
BibRef

Kawata, Y., Kanazawa, K., Toshioka, S., Niki, N., Satoh, H., Ohmatsu, H., Eguchi, K., Moriyama, N.,
Computer Aided Diagnosis System for Lung Cancer Based on Helical CT Images,
CIAP97(II: 420-427).
Springer DOI 9709
BibRef
Earlier: Only: A2, A4, Add: Kubo, M., A5, A6, A7, A8: ICPR96(III: 381-385).
IEEE DOI 9608
(Univ. of Tokushima, J) BibRef

Tozaki, T., Kawata, Y., Niki, N., Ohmatsu, H., Eguchi, K., Moriyama, N.,
Three Dimensional Analysis of Lung Areas Using Thin Slice CT Images,
ICPR96(III: 548-552).
IEEE DOI 9608
(Univ. of Tokushima, J) BibRef

Kawata, Y., Niki, N., Ohmatsu, H., Kakinuma, R., Kushmoto, M., Mori, K., Nishiyama, N., Eguchi, K., Kaneko, M., Moriyama, N.,
Classification of Pulmonary Nodules in Thin-section CT Images by Using Multi-scale Curvature Indexes,
ICIP99(II:197-201).
IEEE DOI BibRef 9900

Kawata, Y., Niki, N., Ohmatsu, H., Eguchi, K., Kaneko, M., and Moriyama, N.,
Classification of Pulmonary Nodules in Thin Section CT Images Based on Shape Characterization,
ICIP97(III: 528-530).
IEEE DOI BibRef 9700

Kawata, Y.[Yoshiki], Kaneko, M., Eguchi, K.[Kenji], Kakinuma, R., Moriyama, N.[Noriyuki], Niki, N.[Noboru], Ohmatsu, H.[Hironobu],
Curvature Based Analysis of Pulmonary Nodules Using Thin-Section CT Images,
ICPR98(Vol I: 361-363).
IEEE DOI 9808
BibRef

Takagi, N., Kawata, Y., Niki, N., Mori, K., Ohmatsu, H., Kakinuma, R., Eguchi, K., Kusumoto, M., Kaneko, M., Moriyama, N.,
3D analysis of solitary pulmonary nodules based on contrast enhanced dynamic CT,
ICIP99(III:416-420).
IEEE DOI BibRef 9900

Tanaka, A., Tozaki, T., Kawata, Y., Niki, N., Ohmatsu, H., Kakimura, R., Kaneko, M., Eguchi, K., Moriyama, N.,
Pulmonary Organs Analysis Method and Its Evaluation Based on Thoracic Thin-section CT Images,
ICIP99(III:421-425).
IEEE DOI BibRef 9900

Mukaibo, T., Kawata, Y., Niki, N., Ohmatsu, H., Kakinuma, R., Kaneko, M., Eguchi, K., Moriyama, K.,
Classification of Pulmonary Blood Vessel Using Multidetector-row CT Images,
ICIP01(II: 841-844).
IEEE DOI 0108
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Tuberculosis Analysis, Tuberculosis Bacilli .


Last update:Mar 16, 2024 at 20:36:19