21.8.3.3 Kidney Disease, Tomography, CAT Analysis, Other Methods

Chapter Contents (Back)
Reconstruction. Kidney Disease. Tomography. Renal analysis
See also Abdominal Seqmentation, Multi-Organ Segmentation.

Gleason, S.S., Sari-Sarraf, H., Abidi, M.A., Karakashian, O., Morandi, F.,
A new deformable model for analysis of X-ray CT images in preclinical studies of mice for polycystic kidney disease,
MedImg(21), No. 10, October 2002, pp. 1302-1309.
IEEE Top Reference. 0301
BibRef

Selfridge, P.G., Prewitt, J.M.S.,
Organ Detection in Abdominal Computerized Tomography Scans: Application to the Kidney,
CGIP(15), No. 3, March 1981, pp. 265-278.
Elsevier DOI BibRef 8103

Oshiro, O.[Osamu], Kamada, K.[Kumi], Imura, M.[Masataka], Chihara, K.[Kunihiro], Toyota, E.[Eiji], Ogasawara, Y.[Yasuo], Kajiya, F.[Fumihiko],
Kidney Glomerulus Observation in Interactive VR Space,
IJIG(3), No. 4, October 2003, pp. 629-637. 0310
BibRef

Xie, J.[Jun], Jiang, Y.F.[Yi-Feng], Tsui, H.T.[Hung-Tat],
Segmentation of kidney from ultrasound images based on texture and shape priors,
MedImg(24), No. 1, January 2005, pp. 45-57.
IEEE Abstract. 0501
BibRef
And: Erratum: MedImg(24), No. 2, February 2005, pp. 277-277.
IEEE Abstract. 0501
BibRef

Linguraru, M.G.[Marius George], Yao, J.H.[Jian-Hua], Gautam, R.[Rabindra], Peterson, J.[James], Li, Z.X.[Zhi-Xi], Linehan, W.M.[W. Marston], Summers, R.M.[Ronald M.],
Renal tumor quantification and classification in contrast-enhanced abdominal CT,
PR(42), No. 6, June 2009, pp. 1149-1161.
Elsevier DOI 0902
Contrast-enhanced CT; Kidney cancer; von Hippel-Lindau syndrome; Hereditary papillary renal carcinoma; Segmentation; Quantification; Classification; Monitoring; Level sets; Computer-assisted radiology BibRef

Raja, K.B.[K. Bommanna], Madheswaran, M., Thyagarajah, K.,
Texture pattern analysis of kidney tissues for disorder identification and classification using dominant Gabor wavelet,
MVA(21), No. 3, April 2010, pp. xx-yy.
Springer DOI 1003
BibRef

Gloger, O., Tonnies, K.D., Liebscher, V., Kugelmann, B., Laqua, R., Volzke, H.,
Prior Shape Level Set Segmentation on Multistep Generated Probability Maps of MR Datasets for Fully Automatic Kidney Parenchyma Volumetry,
MedImg(31), No. 2, February 2012, pp. 312-325.
IEEE DOI 1202
BibRef

Khalifa, F., Beache, G.M., El-Ghar, M.A.[Mohamed A.], El-Diasty, T., Gimel'farb, G.L.[Georgy L.], Kong, M.Y.[Mai-Ying], El-Baz, A.[Ayman],
Dynamic Contrast-Enhanced MRI-Based Early Detection of Acute Renal Transplant Rejection,
MedImg(32), No. 10, 2013, pp. 1910-1927.
IEEE DOI 1311
Laplace equations BibRef

El-Baz, A.[Ayman], Gimel'farb, G.L.[Georgy L.], El-Ghar, M.A.[Mohamed A.],
Image analysis approach for identification of renal transplant rejection,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
A novel image analysis approach for accurate identification of acute renal rejection,
ICIP08(1812-1815).
IEEE DOI 0810
BibRef

Huang, J.[Jie], Yang, X.P.[Xiao-Ping], Chen, Y.M.[Yun-Mei], Tang, L.M.[Li-Ming],
Ultrasound kidney segmentation with a global prior shape,
JVCIR(24), No. 7, 2013, pp. 937-943.
Elsevier DOI 1309
Active contour BibRef

Rudra, A.K.[Ashish K.], Chowdhury, A.S.[Ananda S.], Elnakib, A.[Ahmed], Khalifa, F.[Fahmi], Soliman, A.[Ahmed], Beache, G.[Garth], El-Baz, A.[Ayman],
Kidney segmentation using graph cuts and pixel connectivity,
PRL(34), No. 13, 2013, pp. 1470-1475.
Elsevier DOI 1308
Kidney segmentation BibRef

Skounakis, E., Banitsas, K., Badii, A., Tzoulakis, S., Maravelakis, E., Konstantaras, A.,
ATD: A Multiplatform for Semiautomatic 3-D Detection of Kidneys and Their Pathology in Real Time,
HMS(44), No. 1, February 2014, pp. 146-153.
IEEE DOI 1403
biomedical MRI BibRef

Hodneland, E., Hanson, E.A., Lundervold, A., Modersitzki, J., Eikefjord, E., Munthe-Kaas, A.Z.,
Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys,
IP(23), No. 5, May 2014, pp. 2392-2404.
IEEE DOI 1405
Educational institutions BibRef

Gupta, A.[Abhinav], Karmeshu,
Efficacy of Pearson distributions for characterization of gray levels in clinical ultrasound kidney images,
SIViP(9), No. 6, September 2015, pp. 1317-1334.
WWW Link. 1509
BibRef

Cerrolaza, J.J., Safdar, N., Biggs, E., Jago, J., Peters, C.A., Linguraru, M.G.,
Renal Segmentation From 3D Ultrasound via Fuzzy Appearance Models and Patient-Specific Alpha Shapes,
MedImg(35), No. 11, November 2016, pp. 2393-2402.
IEEE DOI 1609
biological tissues BibRef

Selvathi, D., Bama, S.,
Phase based distance regularized level set for the segmentation of ultrasound kidney images,
PRL(86), No. 1, 2017, pp. 9-17.
Elsevier DOI 1702
Local feature BibRef

Filippi, M., Desvignes, M., Moisan, E.,
Robust Unmixing of Dynamic Sequences Using Regions of Interest,
MedImg(37), No. 1, January 2018, pp. 306-315.
IEEE DOI 1801
blind source separation, feature extraction, image sequences, medical image processing, radioisotope imaging, kidney BibRef

Marsh, J.N., Matlock, M.K., Kudose, S., Liu, T., Stappenbeck, T.S., Gaut, J.P., Swamidass, S.J.,
Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections,
MedImg(37), No. 12, December 2018, pp. 2718-2728.
IEEE DOI 1812
Kidney, Image segmentation, Training, Biological system modeling, Rats, Immune system, Kidney, glomerulosclerosis, digital pathology, donor organ evaluation BibRef

Yu, Q., Shi, Y., Sun, J., Gao, Y., Zhu, J., Dai, Y.,
Crossbar-Net: A Novel Convolutional Neural Network for Kidney Tumor Segmentation in CT Images,
IP(28), No. 8, August 2019, pp. 4060-4074.
IEEE DOI 1907
computerised tomography, image classification, image segmentation, image texture, kidney, CT images BibRef

Gadermayr, M., Gupta, L., Appel, V., Boor, P., Klinkhammer, B.M., Merhof, D.,
Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study on Kidney Histology,
MedImg(38), No. 10, October 2019, pp. 2293-2302.
IEEE DOI 1910
Image segmentation, Training, Adaptation models, Training data, Task analysis, Data models, Pipelines, Histology, image-to-image translation BibRef

Alex, D.M.[Deepthy Mary], Hepzibah Christinal, A., Chandy, D.A.[D. Abraham], Singh, A.[Arvinder], Pushkaran, M.,
Speckle noise suppression in 2D ultrasound kidney images using local pattern based topological derivative,
PRL(131), 2020, pp. 49-55.
Elsevier DOI 2004
Speckle noise, Neighbourhood, Ultrasound, Local pattern, Discrete Topological Derivative, Gradient, Holes BibRef

Ferstl, S., Busse, M., Müller, M., Kimm, M.A., Drecoll, E., Bürkner, T., Allner, S., Dierolf, M., Pfeiffer, D., Rummeny, E.J., Weichert, W., Pfeiffer, F.,
Revealing the Microscopic Structure of Human Renal Cell Carcinoma in Three Dimensions,
MedImg(39), No. 5, May 2020, pp. 1494-1500.
IEEE DOI 2005
Computed tomography, X-ray imaging, Microscopy, Pathology, Tumors, Standards, Attenuation contrast, X-ray imaging BibRef

Li, Y., Polyak, D., Johnson, E., Yecies, D., Shevidi, S., de la Zerda, A., Gephart, M.H., Chu, S.,
Difference-Frequency Ultrasound Imaging With Non-Linear Contrast,
MedImg(39), No. 5, May 2020, pp. 1759-1766.
IEEE DOI 2005
Imaging, Ultrasonic imaging, Tumors, Transducers, Harmonic analysis, Mice, Kidney, Ultrasound, animal models and imaging, image acquisition BibRef

Arulanthu, P.[Pramila], Perumal, E.[Eswaran],
An intelligent IoT with cloud centric medical decision support system for chronic kidney disease prediction,
IJIST(30), No. 3, 2020, pp. 815-827.
DOI Link 2008
Adam, cloud, healthcare, IoT, logistic regression BibRef

Rubini, L.J.[L. Jerlin], Perumal, E.[Eswaran],
Efficient classification of chronic kidney disease by using multi-kernel support vector machine and fruit fly optimization algorithm,
IJIST(30), No. 3, 2020, pp. 660-673.
DOI Link 2008
CKD, fruit fly optimization, healthcare, multi-kernel support vector machine, optimal FS BibRef

Hussain, M.A.[Mohammad Arafat], Hamarneh, G.[Ghassan], Garbi, R.[Rafeef],
Cascaded Regression Neural Nets for Kidney Localization and Segmentation-free Volume Estimation,
MedImg(40), No. 6, June 2021, pp. 1555-1567.
IEEE DOI 2106
Kidney, Location awareness, Image segmentation, Volume measurement, Computed tomography, Sørensen-Dice BibRef

Narasimhulu, C.V.[C Venkata],
An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier,
IET-IPR(15), No. 9, 2021, pp. 2080-2096.
DOI Link 2106
BibRef

Deng, R.N.[Rui-Ning], Yang, H.C.[Hai-Chun], Jha, A.[Aadarsh], Lu, Y.Z.[Yu-Zhe], Chu, P.[Peng], Fogo, A.B.[Agnes B.], Huo, Y.K.[Yuan-Kai],
Map3D: Registration-Based Multi-Object Tracking on 3D Serial Whole Slide Images,
MedImg(40), No. 7, July 2021, pp. 1924-1933.
IEEE DOI 2107
Pathology, Estimation, Deep learning, Strain, Object detection, Imaging, Pathology, renal pathology, MOT, tracking BibRef

Nan, Y.[Yang], Li, F.Y.[Feng-Yi], Tang, P.[Peng], Zhang, G.[Guyue], Zeng, C.H.[Cai-Hong], Xie, G.T.[Guo-Tong], Liu, Z.H.[Zhi-Hong], Yang, G.[Guang],
Automatic fine-grained glomerular lesion recognition in kidney pathology,
PR(127), 2022, pp. 108648.
Elsevier DOI 2205
Deep convolutional neural network, Glomerulus segmentation, Fine-grained lesion classification, Uncertainty assessment, Kidney pathology BibRef

Priyanka, Kumar, D.[Dharmender],
Kidney image classification using transfer learning with convolutional neural network,
IJCVR(12), No. 5, 2022, pp. 595-613.
DOI Link 2211
BibRef

Busse, M., Ferstl, S., Kimm, M.A., Hehn, L., Steiger, K., Allner, S., Müller, M., Drecoll, E., Bürkner, T., Dierolf, M., Gleich, B., Weichert, W., Pfeiffer, F.,
Multi-Scale Investigation of Human Renal Tissue in Three Dimensions,
MedImg(41), No. 12, December 2022, pp. 3489-3497.
IEEE DOI 2212
X-ray imaging, Kidney, Imaging, Histopathology, Computed tomography, Image resolution, Microscopic tissue structure, X-ray staining BibRef

Bugday, M.S.[Muhammet Serdar], Akcicek, M.[Mehmet], Bingol, H.[Harun], Yildirim, M.[Muhammed],
Automatic diagnosis of ureteral stone and degree of hydronephrosis with proposed convolutional neural network, RelieF, and gradient-weighted class activation mapping based deep hybrid model,
IJIST(33), No. 2, 2023, pp. 760-769.
DOI Link 2303
CNN, grad-CAM method, hydroureteronephrosis, RelieF, SVM BibRef

Fathima, M.D.[M. Dhilsath], Hariharan, R., Raja, S.P.,
Multiple Imputation by Chained Equations: K-Nearest Neighbors and Deep Neural Network Architecture for Kidney Disease Prediction,
IJIG(23), No. 2 2023, pp. 2350014.
DOI Link 2303
BibRef

Sheng, Q.[Quan], Zhang, Y.[Yutao], Shi, H.F.[Hai-Feng], Jiao, Z.Q.[Zhu-Qing],
Global iterative optimization framework for predicting cognitive function statuses of patients with end-stage renal disease,
IJIST(33), No. 3, 2023, pp. 837-852.
DOI Link 2305
cognitive function status, end-stage renal disease, functional magnetic resonance imaging, prediction BibRef


Flores-Araiza, D.[Daniel], Lopez-Tiro, F.[Francisco], El-Beze, J.[Jonathan], Hubert, J.[Jacques], Gonzalez-Mendoza, M.[Miguel], Ochoa-Ruiz, G.[Gilberto], Daul, C.[Christian],
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations,
LXCV23(295-304)
IEEE DOI 2309
BibRef

Han, F.[Fuchang], Liao, S.[Shenghui], Yuan, S.[Siming], Wu, R.Z.[Ren-Zhong], Zhao, Y.Q.[Yu-Qian], Xie, Y.[Yu],
Explainable Prediction of Renal Cell Carcinoma from Contrast-Enhanced CT Images Using Deep Convolutional Transfer Learning and the Shapley Additive Explanations Approach,
ICIP21(3802-3806)
IEEE DOI 2201
Analytical models, Additives, Hospitals, Computed tomography, Malignant tumors, Transfer learning, Predictive models, SHapley Additive exPlanations BibRef

Pollastri, F.[Federico], Maroñas, J.[Juan], Bolelli, F.[Federico], Ligabue, G.[Giulia], Paredes, R.[Roberto], Magistroni, R.[Riccardo], Grana, C.[Costantino],
Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification,
ICPR21(1298-1305)
IEEE DOI 2105
Temperature distribution, Biological system modeling, Biopsy, Neural networks, Reliability engineering, Probabilistic logic, BibRef

Casella, A.[Alessandro], Moccia, S.[Sara], Carlini, C.[Chiara], Frontoni, E.[Emanuele], de Momi, E.[Elena], Mattos, L.S.[Leonardo S.],
NephCNN: A deep-learning framework for vessel segmentation in nephrectomy laparoscopic videos,
ICPR21(6144-6149)
IEEE DOI 2105
Laparoscopes, Training, Shape, Surgery, Tools, Convolutional neural networks, kidney segmentation, blood vessel segmentation BibRef

Chi, Y.L.[Yan-Ling], Xu, Y.Y.[Yu-Yu], Feng, G.[Gang], Mao, J.W.[Jia-Wei], Wu, S.[Sihua], Xu, G.[Guibin], Huang, W.M.[Wei-Min],
Segmenting Kidney on Multiple Phase CT Images using ULBNet,
ICPR21(8554-8561)
IEEE DOI 2105
Image segmentation, Computed tomography, Semantics, Surgery, Network architecture, Planning, Pattern recognition, LBC BibRef

Au-Yeung, L.[Lee], Xie, X.H.[Xiang-Hua], Chess, J.[James], Scale, T.[Timothy],
Using Machine Learning to Refer Patients with Chronic Kidney Disease to Secondary Care,
ICPR21(10219-10226)
IEEE DOI 2105
Support vector machines, Interpolation, Sensitivity, Training data, Machine learning, Blood, Medical diagnostic imaging BibRef

Shehata, M., Ghazal, M., Khalifeh, H.A., Khalil, A., Shalaby, A., Dwyer, A.C., Bakr, A.M., Keynton, R., El-Baz, A.,
A Deep Learning-Based CAD System For Renal Allograft Assessment: Diffusion, Bold, And Clinical Biomarkers,
ICIP20(355-359)
IEEE DOI 2011
Kidney, Image segmentation, Biomarkers, Encoding, Magnetic resonance imaging, Machine learning, SAEs BibRef

Bazgir, O., Barck, K., Carano, R.A.D., Weimer, R.M., Xie, L.,
Kidney segmentation using 3D U-Net localized with Expectation Maximization,
SSIAI20(22-25)
IEEE DOI 2009
biomedical MRI, convolutional neural nets, diseases, image segmentation, kidney, medical image processing, Convolutional Neural Network BibRef

Shehata, M., Shalaby, A., Ghazal, M., El-Ghar, M.A., Badawy, M.A., Beache, G., Dwyer, A., El-Melegy, M., Giridharan, G., Keynton, R., El-Baz, A.,
Early Assessment of Renal Transplants Using BOLD-MRI: Promising Results,
ICIP19(1395-1399)
IEEE DOI 1910
Renal transplants, BOLD-MRI, mean R2*, pixel-wise R2*, machine learning BibRef

Wetzer, E.[Elisabeth], Lindblad, J.[Joakim], Sintorn, I.M.[Ida-Maria], Hultenby, K.[Kjell], Sladoje, N.[Nataša],
Towards Automated Multiscale Imaging and Analysis in TEM: Glomerulus Detection by Fusion of CNN and LBP Maps,
BioIm18(VI:465-475).
Springer DOI 1905
BibRef

Mathur, P., Ayyar, M., Shah, R.R., Sharma, S.,
Exploring Classification of Histological Disease Biomarkers From Renal Biopsy Images,
WACV19(81-90)
IEEE DOI 1904
biological tissues, convolutional neural nets, diseases, feature extraction, image classification, kidney, Medical diagnostic imaging BibRef

Abdeltawab, H., Shehatal, M., Shalaby, A., Mesbah, S., El-Baz, M., Ghazal, M., Al Khali, Y., Abou El-Ghar, M., Dwyer, A.C., El-Melegy, M., El-Baz, A.,
A New 3D CNN-based CAD System for Early Detection of Acute Renal Transplant Rejection,
ICPR18(3898-3903)
IEEE DOI 1812
Kidney, Feature extraction, Magnetic resonance imaging, Image segmentation, 3D CNN BibRef

Yang, G.Y.[Guan-Yu], Li, G.Q.[Guo-Qing], Pan, T.[Tan], Kong, Y.Y.[You-Yong], Wu, J.S.[Jia-Song], Shu, H.Z.[Hua-Zhong], Luo, L.M.[Li-Min], Dillenseger, J.L.[Jean-Louis], Coatrieux, J.L.[Jean-Louis], Tang, L.J.[Li-Jun], Zhu, X.M.[Xiao-Mei],
Automatic Segmentation of Kidney and Renal Tumor in CT Images Based on 3D Fully Convolutional Neural Network with Pyramid Pooling Module,
ICPR18(3790-3795)
IEEE DOI 1812
Tumors, Kidney, Computed tomography, Image segmentation, pyramid pooling BibRef

Shehata, M., Ghazal, M., Beache, G., El-Ghar, M.A., Dwyer, A., Hajjdiab, H., Khalil, A., El-Baz, A.,
Role of Integrating Diffusion MR Image-Markers with Clinical-Biomarkers For Early Assessment of Renal Transplants,
ICIP18(146-150)
IEEE DOI 1809
Kidney, Diseases, Image segmentation, Magnetic resonance imaging, Machine learning, Radio frequency, Deep Learning BibRef

Gadermayr, M.[Michael], Klinkhammer, B.M.[Barbara Mara], Boor, P.[Peter], Merhof, D.[Dorit],
Do We Need Large Annotated Training Data for Detection Applications in Biomedical Imaging? A Case Study in Renal Glomeruli Detection,
MLMI16(18-26).
Springer DOI 1611
BibRef

Hussain, M.A.[Mohammad Arafat], Hamarneh, G.[Ghassan], O'Connell, T.W.[Timothy W.], Mohammed, M.F.[Mohammed F.], Abugharbieh, R.[Rafeef],
Segmentation-Free Estimation of Kidney Volumes in CT with Dual Regression Forests,
MLMI16(156-163).
Springer DOI 1611
BibRef

Shehata, M.[Mohamed], Khalifa, F.[Fahmi], Soliman, A.[Ahmed], Alrefai, R.[Rahaf], El-Ghar, M.A.[Mohamed Abou], Dwyer, A.C.[Amy C.], Ouseph, R.[Rosemary], El-Baz, A.[Ayman],
A level set-based framework for 3D kidney segmentation from diffusion MR images,
ICIP15(4441-4445)
IEEE DOI 1512
Adaptive Shape; DW-MRI; Deformable Model BibRef

Khalifa, F., Soliman, A., Dwyer, A.C., Gimel'farb, G., El-Baz, A.,
A random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images,
ICIP16(3399-3403)
IEEE DOI 1610
Computed tomography BibRef

Dai, G.Y.[Gao-Yuan], Li, Z.C.[Zhi-Cheng], Gu, J.[Jia], Wang, L.[Lei], Li, X.M.[Xing-Min],
Segmentation of kidneys from computed tomography using 3D fast GrowCut algorithm,
ICIP13(1144-1147)
IEEE DOI 1402
Accuracy BibRef

Landgren, M.[Matilda], Sjöstrand, K.[Karl], Ohlsson, M.[Mattias], Ståhl, D.[Daniel], Overgaard, N.C.[Niels Christian], Åström, K.[Kalle], Sixt, R.[Rune], Edenbrandt, L.[Lars],
An Automated System for the Detection and Diagnosis of Kidney Lesions in Children from Scintigraphy Images,
SCIA11(489-500).
Springer DOI 1105
BibRef

Khalifa, F., Gimel'farb, G.L., El-Ghar, M.A.[M. Abo], Sokhadze, G., Manning, S., McClure, P., Ouseph, R., El-Baz, A.,
A new deformable model-based segmentation approach for accurate extraction of the kidney from abdominal CT images,
ICIP11(3393-3396).
IEEE DOI 1201
BibRef

Schüffler, P.[Peter], Ulas, A.[Aydin], Castellani, U.[Umberto], Murino, V.[Vittorio],
A Multiple Kernel Learning Algorithm for Cell Nucleus Classification of Renal Cell Carcinoma,
CIAP11(I: 413-422).
Springer DOI 1109
BibRef

Strawn, N.[Nathaniel], Yao, J.H.[Jian-Hua],
Tracking kidney tumor dimensional measurements via image morphing,
ICIP10(1721-1724).
IEEE DOI 1009
BibRef

Pan, T., Yang, G., Wang, C., Lu, Z., Zhou, Z., Kong, Y., Tang, L., Zhu, X., Dillenseger, J., Shu, H., Coatrieux, J.,
A Multi-Task Convolutional Neural Network for Renal Tumor Segmentation and Classification Using Multi-Phasic CT Images,
ICIP19(809-813)
IEEE DOI 1910
Convolution neural network, multi-task, semantic segmentation, medical image processing BibRef

Gadermayr, M.[Michael], Cooper, S.S.[Sean Steven], Klinkhammer, B.M.[Barbara Mara], Boor, P.[Peter], Merhof, D.[Dorit],
A Quantitative Assessment of Image Normalization for Classifying Histopathological Tissue of the Kidney,
GCPR17(3-13).
Springer DOI 1711
BibRef

Abd el Munim, H.E., Farag, A.A.[Aly A.], Miller, W., AboelGhar, M.[Mohamed],
A kidney segmentation approach from DCE-MRI using level sets,
MMBIA08(1-6).
IEEE DOI 0806
BibRef

Boukerroui, D.[Djamal], Touhami, W.[Wala], Cocquerez, J.P.[Jean Pierre],
Automatic regions of interest identification and classification in CT images: Application to kidney cysts,
IPTA08(1-8).
IEEE DOI 0811
BibRef

Koh, H.K., Shen, W.J.[Wei-Jia], Shuter, B., Kassim, A.A.,
Segmentation of Kidney Cortex in MRI Studies using a Constrained Morphological 3D H-maxima Transform,
ICARCV06(1-5).
IEEE DOI 0612
BibRef

Touhami, W., Boukerroui, D., Cocquerez, J.P.,
A Statistical Approach for Automatic Kidneys Detection,
ICIP05(III: 740-743).
IEEE DOI 0512
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Pancreatic Disease, CAT Analysis .


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