Automatic detection of tubules in breast histopathological images

Advances in Intelligent Systems and Computing

Volumn 202 AISC, Issue VOL. 2, 2013, Pages 311-321

  Maqlin, P ^a   Thamburaj, Robinson ^a   Mammen, Joy John ^b   Nagar, Atulya K ^c  

^a MADRAS CHRISTIAN COLLEGE   (India)

^b CHRISTIAN MEDICAL COLLEGE   (India)

^c LIVERPOOL HOPE UNIVERSITY   (United Kingdom)

Author keywords

Breast histopathology; Cancer grading and segmentation; Tubules

Indexed keywords

COMPUTATION THEORY; DIAGNOSIS; DISEASES; GRADING; MEDICAL IMAGING; TISSUE;

BREAST CANCER DETECTION; BREAST HISTOPATHOLOGY; HISTOPATHOLOGICAL EXAMINATIONS; HISTOPATHOLOGICAL IMAGES; LEVEL SET SEGMENTATION; MORPHOLOGICAL FEATURES; SEGMENTATION ALGORITHMS; TUBULES;

IMAGE SEGMENTATION;

EID: 84875110067     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-81-322-1041-2_27     Document Type: Conference Paper

References (14)

1. Beck, J.S.:Observer variability in reporting of breast lesions. J ClinPathol. 38, 1358-1365 (1985)
2. Gurcan, M.N, Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.:Histopathological image analysis: A review. IEEE Reviews in Biomedical Engineering. 2, 147-171 (2009)
3. Bloom, H. J., Richardson, W. W.:British journal of cancer11 (3). 359-377 (1957).
4. Siegel, R., Naishadham, D., Jemal, A.: Cancer Statistics,CA Cancer J. Clin (2012). 62, 10-29. doi: 10.3322/caac.21149
5. Alexe, G., Dalgin,G.S., Scanfeld, D., Tamayo, P., Mesirov, J.P., DeLisi, C., Harri, L., Barnard, N., Martel, M., Levine, A.J., Ganesan, S., Bhanot, G.,High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rate. Cancer Res. 2007, 67(2). 10669-76(2007)
6. Gurcan M.N., Boucheron, L., Can, A., Madabhushi, A., Rajpoot, N.M. and andYener, B, Histopathology Image Analysis: A review, IEEE Rev Biomed Engn. 2:147-171 (2009)
7. Petushi S, Garcia FU, Haber MM, Katsinis C, Tozeren A. 'Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer'. BMC Med Imaging, 6:14.(2006) doi:10.1186/1471-2342-6-14
8. Karvelis, P.S., Fotiadis, D.I., Georgiou, I., Syrrou, M.: A watershed based segmentation method for multispectral chromosome images classification. In:IEEEEng Med BiolSoc, 1. 3009-12 (2006)
9. th IEEE ISBI, Paris, France (2008)
10. Tosun, A.B., Kandemir, M., Sokmensuer, C., and Gunduz-Demir, C.: Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection. Pattern Recognition, 42(6):1104-1112 (2009)
11. thAnnual International Conference of the IEEE, Lyon, France.(2007)
12. Altunbay, D., Cigir, C., Sokmensuer, C., and Gunduz-Demir, C.: Color graphs for automated cancer diagnosis and grading. In: IEEE Transactions on Biomedical Engineering, 57(3):665-674 (2010)
13. Basavanhally, A., Yu, E., Xu, J., Ganesan, S., Feldman, M.D., Tomaszewski, J.E.andMadabhushi, A.: Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods. In: SPIE Medical Imaging (2011).doi: 10.1117/12.878092
14. Li,C., Xu, C., Gui, C. and Fox, M.D.: Level set evolution without re-initialization:a new variational formulation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 430-436. IEEE CVPR (2005)