A classification scheme for lymphocyte segmentation in H&E stained histology images

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6388 LNCS, Issue , 2010, Pages 235-243

  Kuse, Manohar ^a   Sharma, Tanuj ^a   Gupta, Sudhir ^a  

^a LNM INSTITUTE OF INFORMATION TECHNOLOGY   (India)

Author keywords

Classification; Contour Overlap Resolution; Lymphocytes

Indexed keywords

CLASSIFICATION; CLASSIFICATION SCHEME; COLOR APPROXIMATION; CONTOUR OVERLAP RESOLUTION; DIGITAL COLOR IMAGES; EXTRACELLULAR MATRICES; HISTOLOGY IMAGES; HISTOPATHOLOGICAL IMAGES; HSV SPACE; LYMPHOCYTES; MEAN SHIFT; RESOLUTION TECHNIQUES; SVM CLASSIFIERS; TEXTURE FEATURES; THRESHOLDING; TWO-STEP PROCESS; BASED CLUSTERING;

COLOR IMAGE PROCESSING; FEATURE EXTRACTION; SIGNAL PROCESSING; CLASSIFICATION (OF INFORMATION); IMAGE CLASSIFICATION; LYMPHOCYTES; PATTERN RECOGNITION; TELLURIUM COMPOUNDS;

IMAGE SEGMENTATION;

EID: 78650828380     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-17711-8_24     Document Type: Conference Paper

References (19)

1. Bikhet, S.F., Darwish, A.M., Tolba, H.A., Shaheen, S.I.: Segmentation and classification of white blood cells. In: IEEE International Conference on Acostics, Speech and Signal Processing, ICASSP, pp. 550-553 (1992)
2. Cheng, H.D., Shi, X.J., Min, R., Hu, L.M., Cai, X.P., Du., H.N.: Approaches for automated detection and classification of masses in mammograms. Pattern Recogn. 39(4), 646-668 (2006)
3. Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sensing 28, 45-62 (2002)
4. Comaniciu, D.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 58, 71-96 (2002)
5. Deschoolmeester, V., Baay, M., Van Marck, E., Weyler, J., Vermeulen, P., Lardon, F., Vermorken, J.: Tumor infiltrating lymphocytes: an intriguing player in the survival of colorectal cancer patients. BMC Immunology 11(1), 19 (2010)
6. Doughri, R., M'hiri, S., Romdhane, K.B., Ghorbel, F., Essafi, S.: Segmentation and classification of breast cancer cells in hostological images. Information and Communication Technology (2006)
7. Fatakdawala, H., Basavanhally, A., Xu, J., Bhanot, G., Ganesan, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Expectation maximization driven geodesic active contour with overlap resolution: Application to lymphocyte segmentation on breast cancer histopathology. In: International Conference on Bioinformatics and Bioengineering (2009)
8. Fukunaga, Keinosuke, Hostetler, L.D.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory (IEEE) 21(1), 32-40 (1975)
9. PR in HIMA, http://bmi.osu.edu/cialab/ICPR-contest
10. Jemal, A., Siegel, R., Ward, E., Hao, Y., Xu, J., Murray, T., Thun, M.J.: Cancer statistics 2008. CA Cancer J. Clin. 58, 71-96 (2008)
11. Joachims, T.: Making large-scale svm learning practical. advances in kernel methods - support vector learning. MIT-Press, Cambridge (1999)
12. Latson, L., Sebek, B., Powell, K.A.: Automated cell nuclear segmentation in colour images of he stained breast biopsy. Anal. Quant. Cytol. Histol. 25(321-331) (2003)
13. Losi, L., Ponti, G., Di Gregorio, C., Marino, M., Rossi, G., Pedroni, M., Benatti, P., Roncucci, L., de Leon, M.P.: Prognostic significance of histological features and biological parameters in stage i (pt1 and pt2) colorectal adenocarcinoma. Pathology - Research and Practice 202(9), 663-670 (2006) (Pubitemid 44241682)
14. Ongun, G., Halici, U., Leblebiciogl, K., Atalay, V.: Feature extraction and classification of blood cells for an automated differential blood count system. IEEE IJCNN, 2461-2466 (2001)
15. Rasband, W.S.: Imagej. U.S. National Laboratory of Health, Bethesda (1997-2005)
16. Schnorrenberg, F., Pattichis, C., Kyriacou, K., Schizas, C.: Computer-aided detection of breast cancer nuclei. IEEE Transaction on Information Technology in Bio-medicine, 128-140 (1997)
17. Shanmugam, K., Haralick, R.M., Dinstein, I.: Textural features of image classification. IEEE Transactions on Systems, Man and Cybernetics SMC-3, 6 (1973)
18. Soh, L., Tsatsoulis, C.: Texture analysis of sar sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing 37, 2 (1999)
19. Scarff, R.W., Torloni, H.: Histological typing of breast tumors. international classification of tumors. World Health Organization, 13-20 (1968)