21.4.7 Blood Cells, Counting, Extraction, Analysis

Chapter Contents (Back)
Cells. Blood Cells. Leukocyte.

Bacus, J.W., Gose, E.E.,
Leukocyte Pattern Recognition,
SMC(2), No. 4, September 1972, pp. 513-525. BibRef 7209

Bacus, J.W.,
A whitening transformation for two-color blood cell images,
PR(8), No. 1, January 1976, pp. 53-60.
Elsevier DOI 0309
BibRef

Landeweerd, G.H., Gelsema, E.S.,
The Use of Nuclear Texture Parameters in the Automatic Analysis of Leukocytes,
PR(10), No. 2, 1978, pp. 57-61.
Elsevier DOI BibRef 7800

Norgren, P.E.[Philip E.], Kulkarni, A.V.[Ashok V.], Graham, M.D.[Marshall D.],
Leukocyte image analysis in the diff3 system,
PR(13), No. 4, 1981, pp. 299-314.
Elsevier DOI 0309
BibRef

Mui, J.K., and Fu, K.S.,
Automated Classification of Nuecleated Blood Cells Using a Binary Tree Classifier,
PAMI(2), No. 5, September 1980, pp. 429-443. BibRef 8009

Landeweerd, G.H., Gelsema, E.S., Brenner, J.F., Selles, W.D., Zahniser, D.J.,
Pattern Recognition of Nucleated Cells from the Peripheral Blood,
PR(16), No. 2, 1983, pp. 131-140.
Elsevier DOI BibRef 8300
Earlier: PR(15), No. 5, 1982, pp. 425.
Elsevier DOI BibRef

Landeweerd, G.H., Timmers, T., Gelsema, E.S., Bins, M., Halie, M.R.,
Classification of Normal and Abnormal Samples of Peripheral Blood by Linear Mapping of the Feature Space,
PR(16), No. 3, 1983, pp. 319-326.
Elsevier DOI 0309
BibRef

Landeweerd, G.H., Timmers, T., Gelsema, E.S., Bins, M., Halie, M.R.,
Binary Tree Versus Single Level Tree Classification of White Blood Cells,
PR(16), No. 6, 1983, pp. 571-577.
Elsevier DOI 0309
BibRef

Bronkorsta, P.J.H., Reinders, M.J.T., Hendriks, E.A., Grimbergen, J., Heethaar, R.M., Brakenhoff, G.J.,
On-line detection of red blood cell shape using deformable templates,
PRL(21), No. 5, May 2000, pp. 413-424. 0005
BibRef

Theera-Umpon, N.[Nipon], Dougherty, E.R.[Edward R.], Gader, P.D.[Paul D.],
Non-homothetic granulometric mixing theory with application to blood cell counting,
PR(34), No. 12, December 2001, pp. 2547-2560.
Elsevier DOI 0110
BibRef
Earlier: A1, A3, Only:
Training Neural Networks to Count White Blood Cells Via a Minimum Counting Error Objective Function,
ICPR00(Vol II: 299-302).
IEEE DOI 0009
BibRef

di Ruberto, C.[Cecilia], Dempster, A.[Andrew], Khan, S.[Shahid], Jarra, B.[Bill],
Analysis of infected blood cell images using morphological operators,
IVC(20), No. 2, February 2002, pp. 133-146.
Elsevier DOI 0202
BibRef
Earlier:
Segmentation of Blood Images Using Morphological Operators,
ICPR00(Vol III: 397-400).
IEEE DOI
IEEE DOI 0009
BibRef

di Ruberto, C., Dempster, A., Khan, S., Jarra, B.,
Automatic Thresholding of Infected Blood Images Using Granulometry and Regional Extrema,
ICPR00(Vol III: 441-444).
IEEE DOI 0009
BibRef

de Andrade Waldemarin, K.C.[Kátia Cristina], Emílio Beletti, M.[Marcelo], da Fontoura Costa, L.[Luciano],
Nuclear morphometry of neoplastic cells as a method for diagnosis of histiocytoma, mastocytoma and transmissible venereal tumor in dogs,
RealTimeImg(10), No. 4, August 2004, pp. 197-204.
Elsevier DOI 0410
BibRef

Sabino, D.M.U.[Daniela Mayumi Ushizima], da Fontoura Costa, L.[Luciano], Rizzatti, E.G.[Edgar Gil], Zago, M.A.[Marco Antonio],
A texture approach to leukocyte recognition,
RealTimeImg(10), No. 4, August 2004, pp. 205-216.
Elsevier DOI 0410
BibRef

Ray, N., Acton, S.T., Ley, K.,
Tracking leukocytes in vivo with shape and size constrained active contours,
MedImg(21), No. 10, October 2002, pp. 1222-1235.
IEEE Top Reference. 0301
BibRef

Ray, N.[Nilanjan], Acton, S.T.[Scott T.],
Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours,
MedImg(23), No. 12, December 2004, pp. 1466-1478.
IEEE Abstract. 0412
BibRef
Earlier:
Tracking fast-rolling leukocytes in vivo with active contours,
ICIP02(III: 165-168).
IEEE DOI 0210
BibRef

Cui, J.[Jing], Ray, N., Acton, S.T., Lin, Z.[Zongli],
Application of the Affine Transform Invariant Model to Cell Tracking,
Southwest06(56-60).
IEEE DOI 0603
BibRef

Acton, S.T.[Scott T.], Ray, N.[Nilanjan],
Biomedical Image Analysis: Tracking,
Morgan Claypool2006. Synthesis Lectures on Image, Video, and Multimedia Processing
WWW Link. BibRef 0600

Ray, N., Acton, S.T.,
Active contours for cell tracking,
Southwest02(274-278).
IEEE Top Reference. 0208
BibRef

Acton, S.T.,
Biomedical Image Analysis at the Cellular Level,
IMVIP08(27-27).
IEEE DOI 0809
BibRef

Mukherjee, D.P., Ray, N., Acton, S.T.,
Level Set Analysis for Leukocyte Detection and Tracking,
IP(13), No. 4, April 2004, pp. 562-572.
IEEE DOI 0404
BibRef

Dong, G.[Gang], Acton, S.T.,
A variational method for leukocyte detection,
ICIP03(II: 161-164).
IEEE DOI 0312
BibRef

Dong, G.[Gang], Ray, N., Acton, S.T.,
Intravital leukocyte detection using the gradient inverse coefficient of variation,
MedImg(24), No. 7, July 2005, pp. 910-924.
IEEE DOI 0508
BibRef

Zhang, X.W.[Xi-Wen], Song, J.Q.[Ji-Qiang], Lyu, M.R.[Michael R.], Cai, S.J.[Shi-Jie],
Extraction of karyocytes and their components from microscopic bone marrow images based on regional color features,
PR(37), No. 2, February 2004, pp. 351-361.
Elsevier DOI 0311
BibRef

Ahammer, H., Kropfl, J.M., Hackl, C., Sedivy, R.,
Image statistics and data mining of anal intraepithelial neoplasia,
PRL(29), No. 16, 1 December 2008, pp. 2189-2196.
Elsevier DOI 0811
Image statistics; Data mining; Classification; Neoplasia; HIV BibRef

Liu, R.[Ran], Dey, D.K.[Dipak K.], Boss, D.[Daniel], Marquet, P.[Pierre], Javidi, B.[Bahram],
Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis,
JOSA-A(28), No. 6, June 2011, pp. 1204-1210.
WWW Link. 1101
BibRef

Chen, H.M.[Hung-Ming], Tsao, Y.T.[Ya-Ting], Tsai, S.N.[Shin-Ni],
Automatic image segmentation and classification based on direction texton technique for hemolytic anemia in thin blood smears,
MVA(25), No. 2, February 2014, pp. 501-510.
WWW Link. 1402
BibRef

Xia, W.F.[Wen-Fei], Ma, X.P.[Xiao-Peng], Li, X.R.[Xing-Rui], Lu, C.[Chao], Yang, X.W.[Xiao-Wei], Zhu, Z.[Zhi], Yi, J.L.[Ji-Lin],
Reversal effect of low-intensity ultrasound on adriamycin-resistant human hepatoma cells in vitro and in vivo,
IJIST(24), No. 1, 2014, pp. 23-28.
DOI Link 1403
ultrasound, adriamycin-resistant, MDR, hepatoma, HepG2 BibRef

Lee, H.[Howard], Chen, Y.P.P.[Yi-Ping Phoebe],
Cell morphology based classification for red cells in blood smear images,
PRL(49), No. 1, 2014, pp. 155-161.
Elsevier DOI 1410
BibRef

di Ruberto, C.[Cecilia], Loddo, A.[Andrea], Putzu, L.[Lorenzo],
A leucocytes count system from blood smear images,
MVA(27), No. 8, November 2016, pp. 1151-1160.
Springer DOI 1612
BibRef
Earlier:
A Multiple Classifier Learning by Sampling System for White Blood Cells Segmentation,
CAIP15(II:415-425).
Springer DOI 1511
BibRef
And:
Learning by Sampling for White Blood Cells Segmentation,
CIAP15(I:557-567).
Springer DOI 1511
BibRef

di Ruberto, C.[Cecilia], Loddo, A.[Andrea], Putzu, L.[Lorenzo],
Histological Image Analysis by Invariant Descriptors,
CIAP17(I:345-356).
Springer DOI 1711
BibRef

Wang, X.Z.[Xiang-Zhou], Liu, L.[Lin], Du, X.H.[Xiao-Hui], Zhang, J.[Jing], Liu, J.X.[Juan-Xiu], Ni, G.M.[Guang-Ming], Hao, R.Q.[Ru-Qian], Liu, Y.[Yong],
Leukocyte recognition in human fecal samples using texture features,
JOSA-A(35), No. 11, November 2018, pp. 1941-1948.
DOI Link 1912
Edge detection, Feature extraction, Image recognition, Image resolution, Segmentation, Visual system BibRef

Abbasi, M.[Mohamadreza], Kermani, S.[Saeed], Tajebib, A.[Ardeshir], Amin, M.M.[Morteza Moradi], Abbasi, M.[Manije],
Automatic detection of acute lymphoblastic leukaemia based on extending the multifractal features,
IET-IPR(14), No. 1, January 2020, pp. 132-137.
DOI Link 1912
BibRef
And: Corrigendum: IET-IPR(14), No. 5, 17 April 2020, pp. 995-995.
DOI Link 2004
BibRef

Bouchet, A.[Agustina], Montes, S.[Susana], Ballarin, V.[Virginia], Díaz, I.[Irene],
Intuitionistic fuzzy set and fuzzy mathematical morphology applied to color leukocytes segmentation,
SIViP(14), No. 3, April 2020, pp. 557-564.
Springer DOI 2004
BibRef

Shemona, J.S.[Jeya Sudharsan], Chellappan, A.K.[Agees Kumar],
Segmentation techniques for early cancer detection in red blood cells with deep learning-based classifier'a comparative approach,
IET-IPR(14), No. 9, 20 July 2020, pp. 1726-1732.
DOI Link 2007
BibRef

Rao, B.S.[Boyina Subrahmanyeswara],
Accurate leukocoria predictor based on deep VGG-net CNN technique,
IET-IPR(14), No. 10, August 2020, pp. 2241-2248.
DOI Link 2008
BibRef

Pandey, P.[Prashant], Prathosh, A.P., Kyatham, V.[Vinay], Mishra, D.[Deepak], Dastidar, T.R.[Tathagato Rai],
Target-Independent Domain Adaptation for WBC Classification Using Generative Latent Search,
MedImg(39), No. 12, December 2020, pp. 3979-3991.
IEEE DOI 2012
Training, Adaptation models, Task analysis, Microscopy, Biomedical imaging, Cloning, Cameras, WBC, microscopic imaging, VAE BibRef

Joshi, S.[Shivani], Kumar, R.[Rajiv], Dwivedi, A.[Avinash],
Hybrid DSSCS and convolutional neural network for peripheral blood cell recognition system,
IET-IPR(14), No. 17, 24 December 2020, pp. 4450-4460.
DOI Link 2104
BibRef

Omer, A.E.[Ala Eldin], Safavi-Naeini, S.[Safieddin], Hughson, R.[Richard], Shaker, G.[George],
Blood Glucose Level Monitoring Using an FMCW Millimeter-Wave Radar Sensor,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Yan, H.[Hong], Mao, X.Y.[Xuan-Yu], Yang, X.[Xu], Xia, Y.Q.[Yong-Quan], Wang, C.B.[Cheng-Bin], Wang, J.J.[Jun-Jun], Xia, R.[Rui], Xu, X.J.[Xue-Jing], Wang, Z.Q.[Zhi-Qiang], Li, Z.Y.[Zhi-Yang], Zhao, X.[Xie], Li, Y.[Yan], Liu, G.Y.[Guo-Ye], He, L.[Li], Wang, Z.Y.[Zhong-Yu], Wang, Z.Q.[Zhi-Qiong], Li, Z.Q.[Zhi-Qiang], Cai, W.D.[Wei-Dong], Shen, H.[Han], Chang, H.[Hang],
Development and Validation of an Unsupervised Feature Learning System for Leukocyte Characterization and Classification: A Multi-Hospital Study,
IJCV(129), No. 6, June 2021, pp. 1837-1856.
Springer DOI 2106
BibRef

Makkapati, V.V.[Vishnu V.], Spaeth, M., Ulman, S.,
Camera-Projector System for detecting haemoglobin levels during accidents,
US_Patent9,968,282, May 15, 2018.
WWW Link. BibRef 1805

Tomczak, A.[Agnieszka], Ilic, S.[Slobodan], Marquardt, G.[Gaby], Engel, T.[Thomas], Forster, F.[Frank], Navab, N.[Nassir], Albarqouni, S.[Shadi],
Multi-Task Multi-Domain Learning for Digital Staining and Classification of Leukocytes,
MedImg(40), No. 10, October 2021, pp. 2897-2910.
IEEE DOI 2110
Task analysis, Image segmentation, Image reconstruction, Feature extraction, Generators, White blood cells, Microscopy, generative adversarial networks BibRef

Liu, P.[Peirong], Lee, Y.Z.[Yueh Z.], Aylward, S.R.[Stephen R.], Niethammer, M.[Marc],
Perfusion Imaging: An Advection Diffusion Approach,
MedImg(40), No. 12, December 2021, pp. 3424-3435.
IEEE DOI 2112
Estimation, Imaging, Handheld computers, Brain modeling, Mathematical model, Blood, Time series analysis, stroke BibRef

Li, X.Y.[Xin-Yu], Li, M.[Ming], Wu, Y.F.[Yong-Fei], Zhou, X.S.[Xiao-Shuang], Zhang, L.F.[Li-Feng], Ping, X.B.[Xin-Bo], Zhang, X.[Xingna], Zheng, W.[Wen],
Multi-instance inflated 3D CNN for classifying urine red blood cells from multi-focus videos,
IET-IPR(16), No. 8, 2022, pp. 2114-2123.
DOI Link 2205
BibRef

Nurçin, F.V.[Fatih Veysel],
Improved segmentation of overlapping red blood cells on malaria blood smear images with TransUNet architecture,
IJIST(32), No. 5, 2022, pp. 1673-1680.
DOI Link 2209
malaria, overlapping red blood cells, segmentation, TransUNet BibRef

Nozaka, H.[Hiroyuki], Kamata, K.[Kosuke], Yamagata, K.[Kazufumi],
The Effectiveness of Data Augmentation for Mature White Blood Cell Image Classification in Deep Learning: Selection of an Optimal Technique for Hematological Morphology Recognition,
IEICE(E106-D), No. 5, May 2023, pp. 707-714.
WWW Link. 2305
BibRef

Dhar, P.[Prasenjit], Kothandapani, S.D.[Suganya Devi], Satti, S.K.[Satish Kumar], Padmanabhan, S.[Srinivasan],
HPKNN: Hyper-parameter optimized KNN classifier for classification of poikilocytosis,
IJIST(33), No. 3, 2023, pp. 928-950.
DOI Link 2305
anemia, Freeman chain code, KNN classifier, poikilocytosis BibRef

Wu, H.[Huisi], Lin, C.F.[Can-Feng], Liu, J.S.[Jia-Sheng], Song, Y.[Youyi], Wen, Z.K.[Zhen-Kun], Qin, J.[Jing],
Feature Masking on Non-Overlapping Regions for Detecting Dense Cells in Blood Smear Image,
MedImg(42), No. 6, June 2023, pp. 1668-1680.
IEEE DOI 2306
Feature extraction, Task analysis, Cells (biology), Blood, Head, Training, Deep learning, Cell detection, dense detection, non-overlapping region BibRef

Tomczak, A.[Agnieszka], Ilic, S.[Slobodan], Marquardt, G.[Gaby], Engel, T.[Thomas], Navab, N.[Nassir], Albarqouni, S.[Shadi],
Digital Staining of White Blood Cells With Confidence Estimation,
MedImg(42), No. 12, December 2023, pp. 3895-3906.
IEEE DOI 2312
BibRef

Xu, L.Q.[Lin-Quan], Chen, Y.[Yuwen], Lu, S.M.[Song-Mei], Zhong, K.[Kunhua], Li, Y.J.[Yu-Jie], Yi, B.[Bin],
A self-supervised causal feature reinforcement learning method for non-invasive hemoglobin prediction,
IET-IPR(18), No. 1, 2024, pp. 22-33.
DOI Link 2401
biomedical imaging, computer vision, convolutional neural nets, neural nets BibRef

Li, C.[Chongchong], Liu, Y.T.[Yu-Ting],
Improved Generalization of White Blood Cell Classification by Learnable Illumination Intensity Invariant Layer,
SPLetters(31), 2024, pp. 176-180.
IEEE DOI 2401
BibRef

Dsilva, L.R.[Liora Rosvin], Tantri, S.H.[Shivani Harish], Sampathila, N.[Niranjana], Mayrose, H.[Hilda], Bairy, G.M.[G. Muralidhar], Belurkar, S.[Sushma], Saravu, K.[Kavitha], Chadaga, K.[Krishnaraj], Hafeez-Baig, A.[Abdul],
Wavelet scattering- and object detection-based computer vision for identifying dengue from peripheral blood microscopy,
IJIST(34), No. 1, 2024, pp. e23020.
DOI Link 2401
computer vision, dengue, lymphocytes, WST, YOLOv8 BibRef

Atasoy, N.A.[Nesrin Aydin], Rahhawi, A.F.A.A.[Amina Faris Abdulla Al],
Examining the classification performance of pre-trained capsule networks on imbalanced bone marrow cell dataset,
IJIST(34), No. 3, 2024, pp. e23067.
DOI Link 2404
capsule neural network, imbalanced image dataset classification, transfer learning, white blood cell classification BibRef

Gräbel, P.[Philipp], Thull, J.[Julian], Crysandt, M.[Martina], Klinkhammer, B.M.[Barbara M.], Boor, P.[Peter], Brümmendorf, T.H.[Tim H.], Merhof, D.[Dorit],
Automatic Embedding Interventions for the Classification of Hematopoietic Cells,
ICPR22(5104-5110)
IEEE DOI 2212
Representation learning, Neural networks, Bones, Task analysis BibRef

Jamakayala, J.[Jeevan], Gorthi, R.K.S.[Rama Krishna Sai],
Feature Fusion Ensemble Architecture With Active Learning for Microscopic Blood Smear Analysis,
ICIP21(3767-3771)
IEEE DOI 2201
Training, Optical microscopy, Microscopy, Feature extraction, Optical imaging, Labeling, Task analysis, Blood smear, Active Learning BibRef

Wang, Y., Wang, W., van Gastel, M., de Haan, G.,
Modeling on the Feasibility of Camera-Based Blood Glucose Measurement,
CVPM19(1713-1720)
IEEE DOI 2004
Sugar, Blood, Absorption, Scattering, Temperature measurement, Dermis, Camera vital signs, blood glucose, modeling, diabetics BibRef

di Ruberto, C.[Cecilia], Loddo, A.[Andrea], Putzu, L.[Lorenzo],
A Region Proposal Approach for Cells Detection and Counting from Microscopic Blood Images,
CIAP19(II:47-58).
Springer DOI 1909
BibRef

Sjöstrand, E.[Emmy], Jönsson, J.[Jesper], Morell, A.[Adam], Stråhlén, K.[Kent],
Color Normalization of Blood Cell Images,
SCIA19(477-488).
Springer DOI 1906
BibRef

Górriz, M.[Marc], Aparicio, A.[Albert], Raventós, B.[Berta], Vilaplana, V.[Verónica], Sayrol, E.[Elisa], López-Codina, D.[Daniel],
Leishmaniasis Parasite Segmentation and Classification Using Deep Learning,
AMDO18(53-62).
Springer DOI 1807
BibRef

Villamarín, J.A.[Julián A.], Jiménez, Y.M.[Yady M.], Molano, T.[Tatiana], Gutiérrez, E.W.[Edgar W.], Londoño, L.F.[Luis F.], Gutiérrez, D.[David], Montilla, D.[Daniela],
Ultrasonic Assessment of Platelet-Rich Plasma by Digital Signal Processing Techniques,
CIARP17(306-313).
Springer DOI 1802
BibRef

Razzak, M.I., Naz, S.,
Microscopic Blood Smear Segmentation and Classification Using Deep Contour Aware CNN and Extreme Machine Learning,
Microscopy17(801-807)
IEEE DOI 1709
Blood, Diseases, Feature extraction, Image color analysis, Image segmentation, Microscopy, Shape, Blood Sample Analysis, ELM, KWFLICM, RBC, cell morphology, image, analysis BibRef

Carvajal, J., Smith, D.F., Zhao, K., Wiliem, A., Finucane, P., Hobson, P., Jennings, A., McDougall, R., Lovell, B.,
An Early Experience Toward Developing Computer Aided Diagnosis for Gram-Stained Smears Images,
Microscopy17(814-820)
IEEE DOI 1709
Blood, Data mining, Feature extraction, Microorganisms, Microscopy, Pathology BibRef

Dong, Y.F.[Yue-Fang], Fu, W.W.[Wei-Wei], Zhou, Z.[Zhe], Chen, N.[Nian], Liu, M.[Min], Chen, S.[Shi],
ABO blood group detection based on image processing technology,
ICIVC17(655-659)
IEEE DOI 1708
Filling, Image recognition, Image segmentation, ABO blood image, agglutination/no agglutination, blood analysis, standard deviation, threshold, segmentation BibRef

Li, X.[Xiang], Li, W.[Wei], Xu, X.D.[Xiao-Dong], Hu, W.[Wei],
Cell Classification Using Convolutional Neural Networks in Medical Hyperspectral Imagery,
ICIVC17(501-504)
IEEE DOI 1708
Blood, Computer architecture, Hyperspectral imaging, Medical diagnostic imaging, Microprocessors, Support vector machines, blood cell classification, convolutional neural network, deep learning, medical, hyperspectral, imagery BibRef