20.4.2.4 Histopathology, Tissue Analysis

Chapter Contents (Back)
Histopathology.

Chaudhuri, B.B., Rodenacker, K., Burger, G.,
Characterization and Featuring of Histological Section Images,
PRL(7), 1988, pp. 245-252. BibRef 8800

Bartels, P.H., Gahm, T., Thompson, D.,
Automated Microscopy in Diagnostic Histopathology: From Image-Processing to Automated Reasoning,
IJIST(8), No. 2, 1997, pp. 214-223. 9704
BibRef

Adiga, P.S.U.[P.S. Umesh], Chaudhuri, B.B.,
An efficient method based on watershed and rule-based merging for segmentation of 3-D histo-pathological images,
PR(34), No. 7, July 2001, pp. 1449-1458.
Elsevier DOI 0105
BibRef

Gurcan, M.N.[Metin N.], Boucheron, L.[Laura], Can, A.[Ali], Madabhushi, A.[Anant], Rajpoot, N.[Nasir], Yener, B.[Bulent],
Histopathological Image Analysis: A Review,
RevBiomedEng(2), 2009, pp. 147-171.
IEEE DOI
WWW Link. Survey, Histopathology. BibRef 0900

Brenner, J.F.[John F.], Lester, J.M.[James M.], Selles, W.D.[William D.],
Scene Segmentation in Automated Histopathology: Techniques Evolved from Cytology Automation,
PR(13), No. 1, 1981, pp. 65-77.
Elsevier DOI 0309
BibRef

Sertel, O., Kong, J., Shimada, H., Catalyurek, U.V., Saltz, J.H., Gurcan, M.N.,
Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development,
PR(42), No. 6, June 2009, pp. 1093-1103.
Elsevier DOI 0902
Whole-slide histopathological image analysis; Texture analysis; Neuroblastoma BibRef

Kong, J.[Jun], Sertel, O.[Olcay], Shimada, H.[Hiroyuki], Boyer, K.L.[Kim L.], Saltz, J.[Joel], Gurcan, M.N.[Metin N.],
Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation,
PR(42), No. 6, June 2009, pp. 1080-1092.
Elsevier DOI 0902
BibRef
Earlier:
Computer-Aided Grading of Neuroblastic Differentiation: Multi-Resolution and Multi-Classifier Approach,
ICIP07(V: 525-528).
IEEE DOI 0709
Quantitative image analysis; Microscopy images; Neuroblastoma prognosis; Grade of differentiation; Multi-resolution pathological image analysis; Machine learning BibRef

Dundar, M.M.[M. Murat], Badve, S.I.[Sun-Il], Raykar, V.C.[Vikas C.], Jain, R.K.[Rohit K.], Sertel, O.[Olcay], Gurcan, M.N.[Metin N.],
A Multiple Instance Learning Approach toward Optimal Classification of Pathology Slides,
ICPR10(2732-2735).
IEEE DOI 1008
BibRef

Kong, H., Gurcan, M., Belkacem-Boussaid, K.,
Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting,
MedImg(30), No. 9, September 2011, pp. 1661-1677.
IEEE DOI 1109
BibRef

Ali, S., Madabhushi, A.,
An Integrated Region-, Boundary-, Shape-Based Active Contour for Multiple Object Overlap Resolution in Histological Imagery,
MedImg(31), No. 7, July 2012, pp. 1448-1460.
IEEE DOI 1208
BibRef

Loménie, N.[Nicolas], Racoceanu, D.[Daniel],
Point set morphological filtering and semantic spatial configuration modeling: Application to microscopic image and bio-structure analysis,
PR(45), No. 8, August 2012, pp. 2894-2911.
Elsevier DOI 1204
Shape analysis; Mesh analysis; Unorganized point set; Spatial relation modeling; Mathematical morphological operator; Image exploration; Graph representation; Semantic query; Visual reasoning; Digital histopathology BibRef

Srinivas, U., Mousavi, H.S., Monga, V., Hattel, A., Jayarao, B.,
Simultaneous Sparsity Model for Histopathological Image Representation and Classification,
MedImg(33), No. 5, May 2014, pp. 1163-1179.
IEEE DOI 1405
Biomedical image processing BibRef

Gultekin, T., Koyuncu, C.F., Sokmensuer, C., Gunduz-Demir, C.,
Two-Tier Tissue Decomposition for Histopathological Image Representation and Classification,
MedImg(34), No. 1, January 2015, pp. 275-283.
IEEE DOI 1502
biological organs BibRef

Vu, T.H., Mousavi, H.S., Monga, V., Rao, G., Rao, U.K.A.,
Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning,
MedImg(35), No. 3, March 2016, pp. 738-751.
IEEE DOI 1603
Biomedical imaging BibRef

Su, H., Xing, F., Yang, L.,
Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection,
MedImg(35), No. 6, June 2016, pp. 1575-1586.
IEEE DOI 1606
Dictionaries BibRef

Ibragimov, B., Korez, R., Likar, B., Pernuš, F., Xing, L., Vrtovec, T.,
Segmentation of Pathological Structures by Landmark-Assisted Deformable Models,
MedImg(36), No. 7, July 2017, pp. 1457-1469.
IEEE DOI 1707
Computational modeling, Deformable models, Image edge detection, Image segmentation, Laplace equations, Pathology, Shape, Laplacian mesh editing, corpus callosum segmentation, deformablemodels, landmark detection, pathology analysis, prostate segmentation, vertebra, segmentation BibRef

Shi, X.S.[Xiao-Shuang], Sapkota, M.[Manish], Xing, F.Y.[Fu-Yong], Liu, F.J.[Fu-Jun], Cui, L.[Lei], Yang, L.[Lin],
Pairwise based deep ranking hashing for histopathology image classification and retrieval,
PR(81), 2018, pp. 14-22.
Elsevier DOI 1806
Histopathology images, Classification, Retrieval, Ranking hashing, Deep learning BibRef

Zhu, S.J.[Shu-Jin], Li, Y.H.[Yue-Hua], Kalra, S.[Shivam], Tizhoosh, H.R.,
Multiple disjoint dictionaries for representation of histopathology images,
JVCIR(55), 2018, pp. 243-252.
Elsevier DOI 1809
Image retrieval, Image representation, Histopathology, Wholeslide imaging, Bag-of-words, Dictionary learning, LBP, SVM, Deep learning BibRef

Kumar, N.[Neeraj], Uppala, P.[Phanikrishna], Duddu, K.[Karthik], Sreedhar, H.[Hari], Varma, V.[Vishal], Guzman, G.[Grace], Walsh, M.[Michael], Sethi, A.[Amit],
Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering,
MedImg(38), No. 5, May 2019, pp. 1304-1313.
IEEE DOI 1905
Image segmentation, Imaging, Diseases, Spatial resolution, Chemicals, Biological tissues, Quantum cascade lasers, hierarchical clustering BibRef

Lahiani, A.[Amal], Gildenblat, J.[Jacob], Klaman, I.[Irina], Navab, N.[Nassir], Klaiman, E.[Eldad],
Generalising multistain immunohistochemistry tissue segmentation using end-to-end colour deconvolution deep neural networks,
IET-IPR(13), No. 7, 30 May 2019, pp. 1066-1073.
DOI Link 1906
BibRef

Katouzian, A.[Amin], Karamalis, A.[Athanasios], Lisauskas, J.[Jennifer], Eslami, A.[Abouzar], Navab, N.[Nassir],
IVUS-Histology Image Registration,
WBIR12(141-149).
Springer DOI 1208
BibRef

Maji, P., Mahapatra, S.,
Circular Clustering in Fuzzy Approximation Spaces for Color Normalization of Histological Images,
MedImg(39), No. 5, May 2020, pp. 1735-1745.
IEEE DOI 2005
Image color analysis, Histograms, Image analysis, Clustering algorithms, Rough sets, Uncertainty, Fuzzy sets, rough sets BibRef

Li, X.[Xiao], Tang, H.Z.[Hong-Zhong], Zhang, D.B.[Dong-Bo], Liu, T.[Ting], Mao, L.Z.[Li-Zhen], Chen, T.Y.[Tian-Yu],
Histopathological image classification through discriminative feature learning and mutual information-based multi-channel joint sparse representation,
JVCIR(70), 2020, pp. 102799.
Elsevier DOI 2007
Discriminative feature learning, Stack-based discriminative prediction sparse decomposition (SDPSD), Histopathological image classification BibRef

Vu, T., Lai, P., Raich, R., Pham, A., Fern, X.Z., Rao, U.A.,
A Novel Attribute-Based Symmetric Multiple Instance Learning for Histopathological Image Analysis,
MedImg(39), No. 10, October 2020, pp. 3125-3136.
IEEE DOI 2010
Cancer, Image analysis, Training, Task analysis, Support vector machines, Image segmentation, dynamic programming BibRef

Mahmood, F., Borders, D., Chen, R.J., Mckay, G.N., Salimian, K.J., Baras, A., Durr, N.J.,
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images,
MedImg(39), No. 11, November 2020, pp. 3257-3267.
IEEE DOI 2011
Image segmentation, Pathology, Training, Diseases, Task analysis, Generative adversarial networks, Morphology, Nuclei segmentation, synthetic pathology data BibRef

Shafiei, S., Safarpoor, A., Jamalizadeh, A., Tizhoosh, H.R.,
Class-Agnostic Weighted Normalization of Staining in Histopathology Images Using a Spatially Constrained Mixture Model,
MedImg(39), No. 11, November 2020, pp. 3355-3366.
IEEE DOI 2011
Image color analysis, Parameter estimation, Pathology, Gaussian mixture model, Probability density function, spatial information BibRef

Qu, H., Wu, P., Huang, Q., Yi, J., Yan, Z., Li, K., Riedlinger, G.M., De, S., Zhang, S., Metaxas, D.N.,
Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images,
MedImg(39), No. 11, November 2020, pp. 3655-3666.
IEEE DOI 2011
Image segmentation, Annotations, Training, Task analysis, Cancer, Biomedical imaging, Deep learning, Nuclei detection, conditional random field BibRef

Graham, S., Epstein, D., Rajpoot, N.,
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images,
MedImg(39), No. 12, December 2020, pp. 4124-4136.
IEEE DOI 2012
Image segmentation, Standards, Task analysis, Pathology, Harmonic analysis, Computer architecture, Machine learning, computational pathology BibRef

Gunesli, G.N., Sokmensuer, C., Gunduz-Demir, C.,
AttentionBoost: Learning What to Attend for Gland Segmentation in Histopathological Images by Boosting Fully Convolutional Networks,
MedImg(39), No. 12, December 2020, pp. 4262-4273.
IEEE DOI 2012
Glands, Task analysis, Image segmentation, Adaptation models, Training, Boosting, Electronic mail, Deep learning, instance segmentation BibRef

Zheng, Y., Jiang, Z., Xie, F., Shi, J., Zhang, H., Huai, J., Cao, M., Yang, X.,
Diagnostic Regions Attention Network (DRA-Net) for Histopathology WSI Recommendation and Retrieval,
MedImg(40), No. 3, March 2021, pp. 1090-1103.
IEEE DOI 2103
Histopathology, Cancer, Feature extraction, Databases, Solid modeling, Image analysis, Annotations, Digital pathology, RNN BibRef


Zhao, S.[Shuai], Li, X.[Xuanya], Chen, Z.N.[Zhi-Neng], Liu, C.[Chang], Peng, C.G.[Chang-Gen],
Res2-unet: An Enhanced Network for Generalized Nuclear Segmentation in Pathological Images,
MMMod21(II:87-98).
Springer DOI 2106
BibRef

Luo, J.Q.[Jia-Qi], Zhao, Z.C.[Zhi-Cheng], Su, F.[Fei], Guo, L.[Limei],
Triplet-path Dilated Network for Detection and Segmentation of General Pathological Images,
ICPR21(1452-1459)
IEEE DOI 2105
Image segmentation, Pathology, Visualization, Object detection, Feature extraction, Robustness BibRef

Yao, Z.Y.[Ze-Yi], Li, K.Q.[Kai-Qi], Luo, Y.[Yiwen], Zhou, X.G.[Xiao-Guang], Sun, M.[Muyi], Zhang, G.[Guanhong],
Accurate Cell Segmentation in Digital Pathology Images via Attention Enforced Networks,
ICPR21(1590-1595)
IEEE DOI 2105
Pathology, Image segmentation, Solid modeling, Design automation, Image color analysis, Pipelines, Prediction algorithms, digital pathology images BibRef

Shin, B.[Beomjo], Cho, J.[Junsu], Yu, H.[Hwanjo], Choi, S.J.[Seung-Jin],
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning,
ICPR21(4083-4090)
IEEE DOI 2105
Training, Gradient methods, Histopathology, Neural networks, Predictive models, Pattern recognition, Numerical models BibRef

Ozen, Y.[Yigit], Aksoy, S.[Selim], Kösemehmetoglu, K.[Kemal], Önder, S.[Sevgen], Üner, A.[Aysegül],
Self-Supervised Learning with Graph Neural Networks for Region of Interest Retrieval in Histopathology,
ICPR21(6329-6334)
IEEE DOI 2105
Training, Learning systems, Histopathology, Shape, Transfer learning, Image retrieval, Breast, Digital pathology, content-based image retrieval BibRef

Sikaroudi, M.[Milad], Ghojogh, B.[Benyamin], Karray, F.[Fakhri], Crowley, M.[Mark], Tizhoosh, H.R.,
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem,
ICPR21(7080-7086)
IEEE DOI 2105
Training, Histopathology, Training data, Stochastic processes, Gaussian distribution, Bayes methods, Data mining BibRef

Bussola, N.[Nicole], Marcolini, A.[Alessia], Maggio, V.[Valerio], Jurman, G.[Giuseppe], Furlanello, C.[Cesare],
AI Slipping on Tiles: Data Leakage in Digital Pathology,
AIDP20(167-182).
Springer DOI 2103
Reproducible results. BibRef

Sikaroudi, M.[Milad], Ghojogh, B.[Benyamin], Safarpoor, A.[Amir], Karray, F.[Fakhri], Crowley, M.[Mark], Tizhoosh, H.R.[Hamid R.],
Offline Versus Online Triplet Mining Based on Extreme Distances of Histopathology Patches,
ISVC20(I:333-345).
Springer DOI 2103
BibRef

Maleki, D.[Danial], Afshari, M.[Mehdi], Babaie, M.[Morteza], Tizhoosh, H.R.,
Ink Marker Segmentation in Histopathology Images Using Deep Learning,
ISVC20(I:359-368).
Springer DOI 2103
BibRef

Cheng, H.T.[Hsien-Tzu], Yeh, C.F.[Chun-Fu], Kuo, P.C.[Po-Chen], Wei, A.[Andy], Liu, K.C.[Keng-Chi], Ko, M.C.[Mong-Chi], Chao, K.H.[Kuan-Hua], Peng, Y.C.[Yu-Ching], Liu, T.L.[Tyng-Luh],
Self-similarity Student for Partial Label Histopathology Image Segmentation,
ECCV20(XXV:117-132).
Springer DOI 2011
BibRef

Xiang, Y., Chen, J., Liu, Q., Liang, Y.,
Disentangled Representation Learning Based Multidomain Stain Normalization For Histological Images,
ICIP20(360-364)
IEEE DOI 2011
Image color analysis, Image reconstruction, Generative adversarial networks, Training, Decoding, Generators, Deep Learning BibRef

Hosseini, M.S.[Mahdi S.], Chan, L.[Lyndon], Huang, W.M.[Wei-Min], Wang, Y.[Yichen], Hasan, D.[Danial], Rowsell, C.[Corwyn], Damaskinos, S.[Savvas], Plataniotis, K.N.[Konstantinos N.],
On Transferability of Histological Tissue Labels in Computational Pathology,
ECCV20(XXIX: 453-469).
Springer DOI 2010
BibRef

Cheeseman, A.K.[Alison K.], Tizhoosh, H.R.[Hamid R.], Vrscay, E.R.[Edward R.],
Studying the Effect of Digital Stain Separation of Histopathology Images on Image Search Performance,
ICIAR20(II:262-273).
Springer DOI 2007
BibRef

Alinsaif, S., Lang, J.,
Histological Image Classification using Deep Features and Transfer Learning,
CRV20(101-108)
IEEE DOI 2006
Deep learning, Fine-tuning, CNN-Based Features, histopathological, SVM, classification BibRef

Hosseini, M.S.[Mahdi S.], Chan, L.[Lyndon], Tse, G.[Gabriel], Tang, M.[Michael], Deng, J.[Jun], Norouzi, S.[Sajad], Rowsell, C.[Corwyn], Plataniotis, K.N.[Konstantinos N.], Damaskinos, S.[Savvas],
Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning,
CVPR19(11739-11748).
IEEE DOI 2002
BibRef

Hou, L.[Le], Agarwal, A.[Ayush], Samaras, D.[Dimitris], Kurc, T.M.[Tahsin M.], Gupta, R.R.[Rajarsi R.], Saltz, J.H.[Joel H.],
Robust Histopathology Image Analysis: To Label or to Synthesize?,
CVPR19(8525-8534).
IEEE DOI 2002
BibRef

Cheeseman, A.K.[Alison K.], Tizhoosh, H.[Hamid], Vrscay, E.R.[Edward R.],
A Compact Representation of Histopathology Images Using Digital Stain Separation and Frequency-Based Encoded Local Projections,
ICIAR19(II:147-158).
Springer DOI 1909
BibRef

Stanisavljevic, M.[Milos], Anghel, A.[Andreea], Papandreou, N.[Nikolaos], Andani, S.[Sonali], Pati, P.[Pushpak], Rüschoff, J.H.[Jan Hendrik], Wild, P.[Peter], Gabrani, M.[Maria], Pozidis, H.[Haralampos],
A Fast and Scalable Pipeline for Stain Normalization of Whole-Slide Images in Histopathology,
BioIm18(VI:424-436).
Springer DOI 1905
BibRef

Kieffer, B., Babaie, M., Kalra, S., Tizhoosh, H.R.,
Convolutional neural networks for histopathology image classification: Training vs. Using pre-trained networks,
IPTA17(1-6)
IEEE DOI 1804
feature extraction, image classification, image representation, learning (artificial intelligence), medical image processing, medical imaging BibRef

Valkonen, M., Kartasalo, K., Liimatainen, K., Nykter, M., Latonen, L., Ruusuvuori, P.,
Dual Structured Convolutional Neural Network with Feature Augmentation for Quantitative Characterization of Tissue Histology,
BioIm17(27-35)
IEEE DOI 1802
Biological system modeling, Feature extraction, Histograms, Image analysis, Pathology, Training BibRef

Li, W., Qian, X., Ji, J.,
Noise-tolerant deep learning for histopathological image segmentation,
ICIP17(3075-3079)
IEEE DOI 1803
Diseases, Image color analysis, Image segmentation, Machine learning, Muscles, Noise measurement, Training, noisy labels BibRef

Astola, L.[Laura],
Stain separation in digital bright field histopathology,
IPTA16(1-6)
IEEE DOI 1703
biological tissues BibRef

Agarwal, N.[Nitin], Xu, X.M.[Xiang-Min], Gopi, M.,
Automatic Detection of Histological Artifacts in Mouse Brain Slice Images,
MCV16(105-115).
Springer DOI 1711
BibRef

Corredor, G.[German], Romero, E.[Eduardo],
Learning histopathological regions of interest by fusing bottom-up and top-down information,
ICIP15(3200-3204)
IEEE DOI 1512
Histopathology BibRef

Li, X.Y.[Xing-Yu], Plataniotis, K.N.[Konstantinos N.],
Diagnostic color estimation of tissue components in pathology images via von Mises mixture model,
ICIP15(2060-2064)
IEEE DOI 1512
Pathology image BibRef

Hatipoglu, N., Bilgin, G.,
Classification of histopathological images using convolutional neural network,
IPTA14(1-6)
IEEE DOI 1503
image classification BibRef

McCann, M.T.[Michael T.], Majumdar, J.[Joshita], Peng, C.[Cheng], Castro, C.A.[Carlos A.], Kovacevic, J.[Jelena],
Algorithm and benchmark dataset for stain separation in histology images,
ICIP14(3953-3957)
IEEE DOI 1502
Accuracy BibRef

Sommer, C.[Christoph], Fiaschi, L.[Luca], Hamprecht, F.A.[Fred A.], Gerlich, D.W.[Daniel W.],
Learning-based mitotic cell detection in histopathological images,
ICPR12(2306-2309).
WWW Link. 1302
BibRef

Toutain, M., Lézoray, O., Audigié, F., Busoni, V., Rossi, G., Parillo, F., El Moataz, A.,
Analysis of Whole Slide Images of Equine Tendinopathy,
ICIAR12(II: 440-447).
Springer DOI 1206
BibRef

Díaz, G.[Gloria], Romero, E.[Eduardo],
Histopathological Image Classification Using Stain Component Features on a pLSA Model,
CIARP10(55-62).
Springer DOI 1011
BibRef

Cooper, L.[Lee], Saltz, J.[Joel], Machiraju, R.[Raghu], Huang, K.[Kun],
Two-point correlation as a feature for histology images: Feature space structure and correlation updating,
MMBIA10(79-86).
IEEE DOI 1006
BibRef

Graf, F.[Felix], Grzegorzek, M.[Marcin], Paulus, D.[Dietrich],
Counting Lymphocytes in Histopathology Images Using Connected Components,
ICPR-Contests10(263-269).
Springer DOI 1008
BibRef

Cheng, J.[Jierong], Veronika, M.[Merlin], Rajapakse, J.C.[Jagath C.],
Identifying Cells in Histopathological Images,
ICPR-Contests10(244-252).
Springer DOI 1008
BibRef

Kuse, M.[Manohar], Sharma, T.[Tanuj], Gupta, S.[Sudhir],
A Classification Scheme for Lymphocyte Segmentation in H&E Stained Histology Images,
ICPR-Contests10(235-243).
Springer DOI 1008
BibRef

Gurcan, M.N.[Metin N.], Madabhushi, A.[Anant], Rajpoot, N.[Nasir],
Pattern Recognition in Histopathological Images: An ICPR 2010 Contest,
ICPR-Contests10(226-234).
Springer DOI 1008
BibRef

Thomas, K.A.[Kristine A.], Sottile, M.J.[Matthew J.], Salafia, C.M.[Carolyn M.],
Unsupervised Segmentation for Inflammation Detection in Histopathology Images,
ICISP10(541-549).
Springer DOI 1006
BibRef

Noah, S.A.[Shahrul Azman], Yaakob, S.[Suraya], Shahar, S.[Suzana],
Application of Information Visualization Techniques in Representing Patients' Temporal Personal History Data,
IVIC09(168-179).
Springer DOI 0911
BibRef

Cosatto, E.[Eric], Miller, M.[Matt], Graf, H.P.[Hans Peter], Meyer, J.S.[John S.],
Grading nuclear pleomorphism on histological micrographs,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Canada, B.A.[Brian A.], Thomas, G.K.[Georgia K.], Cheng, K.C.[Keith C.], Wang, J.Z.[James Z.], Liu, Y.X.[Yan-Xi],
Automatic lattice detection in near-regular histology array images,
ICIP08(1452-1455).
IEEE DOI 0810
BibRef
And:
Towards efficient automated characterization of irregular histology images via transformation to frieze-like patterns,
CIVR08(581-590). 0807
BibRef

Zhao, D.H.[De-Hua], Chen, Y.X.[Yi-Xin], Correa, H.,
Statistical Categorization of Human Histological Images,
ICIP05(III: 628-631).
IEEE DOI 0512
BibRef

Roula, M.A., Bouridane, A., Kurugollu, F.,
An evolutionary snake algorithm for the segmentation of nuclei in histopathological images,
ICIP04(I: 127-130).
IEEE DOI 0505
BibRef

Nedzved, A., Ablameyko, S.V., Pitas, I.,
Morphological Segmentation of Histology Cell Images,
ICPR00(Vol I: 500-503).
IEEE DOI 0009
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Fluorescence Analysis, Microscopic Analysis, Cells .


Last update:Jun 14, 2021 at 09:20:36