Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte

  • John Arevalo Universidad Nacional de Colombia
  • Angel Cruz-Roa Universidad Nacional de Colombia
  • Fabio A. González O Universidad Nacional de Colombia
Palabras clave: histopathology, image analysis, computer-assisted, pattern recognition system, informatics computing, medical, state-of-the-art review

Resumen

This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of recent works and methods addressed to deal with visual content representation in different automatic image analysis tasks is presented. Third an overview of applications of image representation methods in several medical domains and tasks is presented. Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology.

Biografía del autor/a

John Arevalo, Universidad Nacional de Colombia
M.Sc. Universidad Nacional de Colombia, Ph.D. Student
Angel Cruz-Roa, Universidad Nacional de Colombia
M.Sc. Universidad Nacional de Colombia, Ph.D. Candidate
Fabio A. González O, Universidad Nacional de Colombia

Ph.D., Associate professor, Universidad Nacional de Colombia, Computing systems and industrial engineering dept.

Descargas

Los datos de descargas todavía no están disponibles.

Lenguajes:

in

Biografía del autor/a

John Arevalo, Universidad Nacional de Colombia
M.Sc. Universidad Nacional de Colombia, Ph.D. Student
Angel Cruz-Roa, Universidad Nacional de Colombia
M.Sc. Universidad Nacional de Colombia, Ph.D. Candidate
Fabio A. González O, Universidad Nacional de Colombia

Ph.D., Associate professor, Universidad Nacional de Colombia, Computing systems and industrial engineering dept.

Referencias bibliográficas

Fuchs TJ, Buhmann JM. Computational pathology: challenges and promises for tissue analysis. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 2011;35(7–8): 515–30

Hipp JD, Smith SC, Sica J, Lucas D, Hipp JA, Kunju LP, et al. Tryggo: Old norse for truth: The real truth about ground truth: New insights into the challenges of generating ground truth maps for WSI CAD algorithm evaluation. Journal of pathology informatics. 2012;3(1): 8

He L, Rodney Long L, Antani S, Thoma GR. Histology image analysis for carcinoma detection and grading. Computer Methods and Programs in Biomedicine. 2012

Gurcan M, Boucheron L, Can A, Madabhushi A, Rajpoot NM, Yener B. Histopathological image analysis: a review. IEEE reviews in biomedical engineering. 2009;2: 147–71

Naghdy G, Ros M, Todd C, Norahmawati E. Cervical Cancer Classification Using Gabor Filters in 2011. IEEE First International Conference on Healthcare Informatics. Imaging and Systems Biology. 2011

Tosun AB, Kandemir M, Sokmensuer C, Gunduz-Demir C. Object- oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection. Pattern Recognition. 2009;42(6): 1104–1112

Monaco JP, Tomaszewski JE, Feldman MD, Hagemann I, Moradi M, Mousavi P, et al. High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models. Medical Image Analysis. 2010;14(4): 617–629

Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J. Automated grading of breast cancer histopathology using spectral clusteringwith textural and architectural image features. In 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2008. p. 496–499.

Le Bozec C, Jaulent M, Zapletal E, Heudes D, Degoulet P. A visual coding system in histopathology and its consensual acquisition. Proceedings of the AMIA Symposium. 1999. p. 306.

Cooper L. High performance image analysis for large histological datasets. 2009.

Basu S. Some upcomming Challenges in Bioimage Informatics. 2012.

Kalfoglou Y, Dasmahapatra S, Dupplaw D, Hu B, Lewis P, Shadbolt N. Living with the semantic gap: Experiences and remedies in the context of medical imaging. 2006.

Hewitson T, Darby IA. Histology Protocols., vol. 611. Totowa, NJ: Humana Press; 2010

Díaz G. Semantic Information Extraction from Microscopy Medical Images. National University of Colombia. 2010.

Kiernan J. Histological and Histochemical Methods: Theory and Practice. 4th ed. Cold Spring Harbor Laboratory Press; 2008. p. 606.

Lowe DG. Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference. 1999;2: 1150–1157.

Alpaydin E. Introduction to Machine Learning. 2nd ed. The MIT Press; 2010.

Ozdemir E, Sokmensuer C, Gunduz-Demir C. A resampling-based Markovian model for automated colon cancer diagnosis. IEEE transactions on bio-medical engineering. 2012; 59(1): 281–9

Demir C, Yener B. Automated cancer diagnosis based on histopathological images: a systematic survey. Rensselaer Polytechnic Institute. 2005

Samsi S, Lozanski G, Shanarah A, Krishanmurthy AK, Gurcan MN. Detection of Follicles From IHC-Stained Slides of Follicular Lymphoma Using Iterative Watershed. IEEE Transactions on Biomedical Engineering. 2010; 57(10): 2609–2612

Samsi S, Krishnamurthy AK, Gurcan MN. An efficient computational framework for the analysis of whole slide images: Application to follicular lymphoma immunohistochemistry. Journal of Computational Science. 2012

Mete M, Topaloglu U. Statistical comparison of color model-classifier pairs in hematoxylin and eosin stained histological images. En 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology; 2009. p. 284–291.

Mahmoud-Ghoneim D. Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions. Theoretical biology & medical modelling. 2011;8: 25

DiFranco MD, O’Hurley G, Kay EW, Watson R, Cunningham P. Ensemble based system for whole-slide prostate cancer probability mapping using color texture features. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 2010;35(7-8): 629–45

Dundar M, Badve S, Bilgin G, Raykar V, Jain R, Sertel O, et al. Computerized classification of intraductal breast lesions using histopathological images. IEEE Transactions on Biomedical Engineering. 2011;58(7): 1977–1984

Muthu Rama Krishnan M, Chakraborty C, Paul R, Ray AK. Hybrid segmentation, characterization and classification of basal cell nuclei from histopathological images of normal oral mucosa and oral submucous fibrosis. Expert Systems with Applications. 2012; 39(1): 1062–1077

Dangott B, Salama , Ramesh N, Tasdizen T. Isolation and twostep classification of normal white blood cells in peripheral blood smears. Journal of Pathology Informatics. 2012;3(1): 13, 2012.

Sparks R, Madabhushi A, Sparks MA. Content-based image retrieval utilizing explicit shape descriptors: applications to breast MRI and prostate histopathology. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE;2011. p. 79621I–79621I–13.

Naik S, Doyle S, Madabhushi A, Tomaszewski J, Feldman M. Automated Gland Segmentation and Gleason Grading of Prostate Histology by Integrating Low-, High-level and Domain Specific Information. Workshop on Microscopic Image Analysis with Applications in Biology; 2007.

Basavanhally A, Ganesan S, Shih N, Mies C, Feldman M, Tomaszewski J, et al. A boosted classifier for integrating multiple fields of view: Breast cancer grading in histopathology. Proceedings - International Symposium on Biomedical Imaging; 2011. p. 125–128.

Madabhushi A, Agner S, Basavanhally A, Doyle S, Lee G. Computer- aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 2011;35(7): 506–14

Haralick R, Shanmugam K, Dinstein I. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973;3(6): 610–621

Kuse M, Sharma T, Gupta S. A Classification Scheme for Lymphocyte Segmentation in H&E Stained Histology Images. In Recognizing Patterns in Signals, Speech, Images and Videos, vol. 6388, D. Ünay, Z. Çataltepe, and S. Aksoy, Eds. Springer Berlin / Heidelberg; 2010. p. 235–243.

Chaddad A, Tanougast C, Dandache A, Al Houseini A, Bouridane A. Improving of colon cancer cells detection based on Haralick’s features on segmented histopathological images. IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE); 2011. p. 87–90.

Cinar Akakin H, Gurcan M. Content-based Microscopic Image Retrieval System for Multi-Image Queries.IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society; 2012.

Caicedo J, Cruz-Roa A, Gonzalez F. Histopathology image classification using bag of features and kernel functions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2009;5651(LNAI): 126–135

Caicedo J, Gonzalez F, Romero E. Content-based histopathology image retrieval using a kernel-based semantic annotation framework. Journal of Biomedical Informatics. 2011;44(4): 519–528

Cruz-Roa A, Caicedo J, González F. Visual pattern mining in histology image collections using bag of features.Artificial intelligence in medicine. 2011;52(2): 91–106

Díaz G, Romero E. Histopathological Image Classification Using Stain Component Features on a pLSA Model. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, vol. 6419, I. Bloch and R. Cesar, Eds. Springer Berlin / Heidelberg; 2010. p. 55–62.

Cruz-Roa A, Caicedo J, Gonzalez F. Visual pattern analysis in histopathology images using bag of features. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2009;5856(LNCS): 521–528

Galaro J, Judkins AR, Ellison D, Baccon J, Madabhushi A. An integrated texton and bag of words classifier for identifying anaplastic medulloblastomas. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society Conference; 2011. p. 3443–6

Raza S, Parry RM, Sharma Y, Chaudry Q, Moffitt RA, Young A, et al. Automated classification of renal cell carcinoma subtypes using bag-of-features. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC; 2010. p. 6749–6752

Vanegas J, Caicedo J, Gonzalez F, Romero E. Histology Image Indexing Using a Non-negative Semantic Embedding. In Medical Content-Based Retrieval for Clinical Decision Support, vol. 7075, H. Müller, H. Greenspan, and T. Syeda- Mahmood, Eds. Springer Berlin / Heidelberg; 2012. p. 80–91

Krishnan M, Venkatraghavan V, Acharya U, Pal M, Paul R, Min L, et al. Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm.Micron. 2012;43(2): 352–64

Krishnan MR, Shah P, Choudhary A, Chakraborty C, Paul RR, Ray AK. Textural characterization of histopathological images for oral sub-mucous fibrosis detection.Tissue & cell. 2011;43(5): 318–30

Ministerio de Salud y Protección Social (Colombia). Guías de atención integral en cancer; 2012

Peng Y, Jiang Y, Eisengart L, Healy MA, Straus FH, Yang XJ. Computer-aided identification of prostatic adenocarcinoma: Segmentation of glandular structures. Journal of pathology informatics. 2011;2: 33

Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J. Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2008. p. 284–287.

Almuntashri A, Agaian S, Thompson I, Rabah D, Zin Al-Abdin O, Nicolas M. Gleason grade-based automatic classification of prostate cancer pathological images. IEEE International Conference on Systems, Man, and Cybernetics; 2011. p. 2696– 2701.

Khurd P, Bahlmann C, Maday P, Kamen A, Gibbs-Strauss S, Genega EM, et al. Computer-aided Gleason grading of prostate cancer histopathological images using texton forest. Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging. 2010;14: 636–639

Ministerio de Salud y Protección Social and E.S.E.Instituto Nacional de Cancerología (Colombia). Anuario estadístico 2010. Instituto nacional de cancerología; 2012.

Wang Y, Crookes D, Eldin OS, Wang S, Hamilton P, Diamond J. Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN). IEEE Journal of Selected Topics in Signal Processing. 2009;3(1): 112–121

Zhang L, Chen S, Wang T, Chen Y, Liu S, Li M. A Practical Segmentation Method for Automated Screening of Cervical Cytology. International Conference on Intelligent Computation and Bio-Medical Instrumentation; 2011. p. 140–143.

He L, Rodney L, Antani S, Thomas GR. Local and global Gaussian mixture models for hematoxylin and eosin stained histology image segmentation. International Conference on Hybrid Intelligent Systems; 2010. p. 223–228.

Jing F, Li MJ, Zhang HJ, Zhang B. Unsupervised image segmentation using local homogeneity analysis,International Symposium on Circuits and Systems. 2003;2: II–456–II–459

Miller SJ, Alam M, Andersen J, Berg D, Bichakjian CK, Bowen G, et al. Basal cell and squamous cell skin cancers. Journal of the National Comprehensive Cancer Network. 2010;8(8): 836–864

Wong CS, Strange RC, Lear JT. Basal cell carcinoma.BMJ Clinical research. 2003;327: 794–8

Camargo J, Caicedo J, Gonzalez F. Kernel-Based Visualization of Large Collections of Medical Images Involving Domain Knowledge. X Congreso Internacional de Interaccion Persona- Ordenador; 2009

Cruz-Roa A, Romero E, Gonzalez F, Diaz G. Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization. Journal of Pathology Informatics. 2011;2(2): 4

Cruz-Roa A, Diaz G, Gonzalez F. A framework for semantic analysis of histopathological images using nonnegative matrix factorization. Computing Congress (CCC), 2011 6th Colombian; 2011. p. 1–7.

Gutiérrez R, Gómez F, Roa-Peña L, Romero E. A supervised visual model for finding regions of interest in basal cell carcinoma images.Diagnostic pathology. 2011;6: 26

Díaz G, Romero E. Micro-structural tissue analysis for automatic histopathological image annotation. Microscopy research and technique. 2011;75(3): 343–58

Hipp JD, Sica J, McKenna B, Monaco J, Madabhushi A, Cheng J, et al. The need for the pathology community to sponsor a whole slide imaging repository with technical guidance from the pathology informatics community. Journal of pathology informatics. 2011;2: 31

Ghaznavi F, Evans AJ, Madabhushi A, Feldman MD. Digital Imaging in Pathology: Whole-Slide Imaging and Beyond. Annual Review of Pathology: Mechanisms of Disease. 2012;8: 1

Madabhushi A. Digital pathology image analysis: opportunities and challenges. Imaging in Medicine. 2009;1: 7–10

Hipp J, Cheng J, Daignault S, Sica J, Dugan MC, Lucas M, et al. Automated area calculation of histopathologic features using SIVQ. Analytical cellular pathology. 2011;34(5): 265–75

Nguyen K, Sabata B, Jain AK. Prostate cancer grading: Gland segmentation and structural features. Pattern Recognition Letters. 2012;33(7): 951–961

Loménie N, Racoceanu D. Point set morphological filtering and semantic spatial configuration modeling: Application to microscopic image and bio-structure analysis. Pattern Recognition. 2012;45(8): 2894–2911

Basavanhally AN, Ganesan S, Agner S, Monaco JP, Feldman MD, Tomaszewski JE, et al. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE transactions on bio-medical engineering. 2010;57(3): 642–53

Muthu Rama M, Pal M, Bomminayuni SK, Chakraborty C, Paul RR, Chatterjee J, et al. Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis- an SVM based approach. Computers in biology and medicine. 2009;39(12): 1096–104

Lomenie N, Racoceanu D. Spatial relationships over sparse representations. In 2009 24th International Conference Image and Vision Computing New Zealand; , 2009.

Xu J, Janowczyk A, Chandran S, Madabhushi A. A high-throughput active contour scheme for segmentation of histopathological imagery. Medical image analysis. 2011;15(6): 851–62

Simsek A, Tosun A, Aykanat C, Sokmensuer C, Gunduz-Demir C. Multilevel Segmentation of Histopathological Images using Cooccurrence of Tissue Objects. IEEE transactions on bio-medical engineering. 2012;99: 1

Ozdemir E, Sokmensuer C, Gunduz-Demir C. Histopathological image classification with the bag of words model. EEE 19th Signal Processing and Communications Applications Conference (SIU); 2011. p. 634–637

Lundin M, Lundin L, Helin H, Isola J. A digital atlas of breast histopathology: an application of web based virtual microscopy. Journal of Clinical Pathology. 200;57(12): 1288–1291

Muthu Rama M, Shah P, Chakraborty C, Ray A. Statistical Analysis of Textural Features for Improved Classification of Oral Histopathological Images. Journal of Medical Systems. 2012;36(2): 865–881

Nguyen K, Sabata B, Jain A. Prostate cancer detection: Fusion of cytological and textural features. Journal of Pathology Informatics. 2011;2(2): 3

Cómo citar
Arevalo, J., Cruz-Roa, A., & González O, F. A. (2014). Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte. Revista Med, 22(2), 79–91. https://doi.org/10.18359/rmed.1184
Publicado
2014-12-01
Sección
Artículos

Métricas

QR Code