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Estrogen Receptor (ER) Cell Detection in Breast Cancer Using Modified Watershed Algorithm

Prasanna G. Shete, .Dr. Gajanan K. Kharate, Sanket C. Rege

Abstract


The paper discusses an approach involving digital image processing for estimating the breast cancer cell population in breast tissue sample. The process aims at providing a reliable,repeatable, and fast method that could replace the traditional methodof manual examination and subsequent estimation. The marker discussed in the paper is the Estrogen Receptor (ER) that gives a clear indication of the presence of cancer cells in the tissue sample. The methods involved are HSV color conversion from RGB image, Hue, saturation and value based object-background separation, morphological operations such as dilation and closing, and area based filtering for preliminary preparation of image for detailed analysis. A modified watershed algorithm designed for eliminating errors arising due to over-segmentation in traditional watershed algorithm is proposed to provide comparatively more accurate results. Further, intensity based thresholding is performed for identifying andcategorizing the cancerous cells into levels of severity of damage done to cells due to cancer. The proposed modified watershed algorithm is compared with the original watershed algorithm and an accuracy of almost 96.44% was observed and verified.


Keywords


Cancer Cells, Estrogen Receptor, HSV Model Based Object-Background Separation, Intensity Based Cancer Cell Counting, Modified Watershed Algorithm.

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References


Pornchai Phukpattaranont and Pleumjit Boonyaphiphat, “Color Based Segmentation of Nuclear Stained Breast Cancer Cell Images”, in ECTI Transactions on Electrical Engineering, Electronics, and Communications, vol. 5, no. 2, pp. 158, August 2007.

J. P. Thiran and B. Macq, “Morphological feature extraction for the classification of digital images of cancerous tissues”, in IEEE Transactions on Biomedical Engineering., vol. 43, no. 10, pp. 1011- 1020, October 1996.

B. Fang, W. Hsu, and M. L. Lee, “On the accurate counting of tumor cells”, in IEEE Transactions on Nanobioscience., vol. 2, no. 2, pp. 94- 103, June 2003.

S. Petushi, C. Katsinis, C. Coward, F. Garcia, and A. Tozeren, “Automated identification of microstructures on histology slides”, in IEEE International Symposium on Biomedical Imaging : Macro to Nano, vol. 1, pp. 424-427, 2004.

P. L. Fitzgibbons, D. L. Page, D. Weaver, A. D. Thor, D. C. Allred, G.M. Clark, S. G. Ruby, F. O’Malley, J. F. Simpson, J. L. Connolly, D.F. Hayes, S. B. Edge, A. Lichter, and S. J. Schnitt, “Prognostic factors in breast cancer: College of americal pathologists consensus statement 1999”, in Archives of Pathology and Laboratory Medicine, vol. 124, pp. 966–978, 2000.

J. S. Ross, G. P. Linette, J. Stec, E. Clark, M. Ayers, N. Leschly, W. F. Symmans, G. N. Hortobagyi, and L. Pusztai, “Breast cancer biomarkers and molecular medicine”, in Expert Review of Molecular Diagnostics, vol. 3, pp. 573–585, 2003.

M. Colozza, E. Azambuja, F. Cardoso, C. Sotiriou, D. Larsimont, and M. J. Piccart, “Proliferative markers as prognostic and predictive tools in early breast cancer: Where are we now”, in Annals of Oncology, vol. 16,pp. 1723–1739, 2005.

S. P. Linke, T. M. Bremer, C. D. Herold, G. Sauter, and C. Diamond, “A multi-marker model to predict outcome in tamoxifen-treated breast cancer patients”, in Clinical Cancer Research, vol. 12, pp. 1175–1183, 2006.

H. Yamashita, M. Nishio, T. Toyoma, H. Sugiura, Z. Zhang, S. Kobayashi, and H. Iwase, “Coexistence of HER2 over-expression and p53 protein accumulation is a strong prognostic molecular marker in breast cancer”, in Breast Cancer Research, vol. 6, pp. 24–30, 2004.

D. Coradini and M. G. Daidone, “Bio-molecular prognostic factors in breast cancer”, in Current Opinion Obstetrics Gynaecology, vol. 16, pp. 49–55, 2004.

S. Bose, S. Chandran, J. M. Mirocha, and N. Bose, “The AKT pathway in human breast cancer: A tissue-array-based analysis”, in Modern Pathology, vol. 19, pp. 238–245, 2006.

G. Perez-Tenorio and O. Stal, “Activation of AKT/PKB in breast cancer predicts a worse outcome among endocrine treated patients”, in British Journal of Cancer, vol. 86, pp. 540–545, 2002.

K. J. Schmitz, F. Otterbach, R. Callies, B. Levkau, M. Holscher, O. Hoffmann, F. Grabellus, R. Kimmig, K. W. Schmid, and H. A. Baha,“Prognostic relevance of activated AKT kinase in node-negative breast cancer: A clinic-pathological study of 99 cases”, in Modern Pathology, vol. 17, pp. 15–21, 2004.

J. N. Hutchinson, J. Jin, R. D. Cardiff, J. R.Woodgett, and W. J. Muller, “Activation of AKT-1 (PKB-a) can accelerate ErbB-2 mediated mammary tumotigenesis but suppresses tumour invasion”, in Cancer Research, vol. 64, pp. 3171–3178, 2004.

S.Wullschleger, R. Loewith, and M. N. Hall, “TOR signalling in growth and metabolism”, in Cell, vol. 124, pp. 471–484, 2006.

Hela Masmoudi, Stephen M. Hewitt, Nicholas Petrick, Kyle J. Myers, and Marios A. Gavrielides, “Automated Quantitative Assessment of HER-2/neu Immuno-histo-chemical Expression in Breast Cancer”, in IEEE transactions on medical imaging, vol. 28, no. 6, pp. 916, June 2009.

R. A. Walker, “Immuno-histo-chemical markers as predictive tools for breast cancer”, in Journal of Clinical Pathology, pp. 689-696, originally published online November 23, 2007.

Z. Ahmed, N. S. Azad, Y. Bhurgari, R. Ahmed, N. Kayani, S. Pervez, and S. Hasan, “Significance of immune-histo-chemistry in accurate characterization of malignant tumours”, in J. Ayub. Medical College, Abbottabad, vol. 18, pp. 38–43, 2006.

Z. Theodosiou, I. Kasampalidis, G. Livanos, M. Zervakis, I. Pitas, and K. Lyroudia, “Automated analysis of fish and immuno-histo-chemistry images: A review”, in Cytometry A, vol. 71, pp. 459–450, 2007.

H. S. Wu and J. Barba, “An algorithm for noisy cell contour extraction via area merging”, in Journal of Imaging Science and Technology, vol. 38, pp. 604-607, November 1994.

Marios A. Gavrielides, Hela Masmoudi, Nicholas Petrick, Kyle J. Myers, and Stephen M. Hewitt, “Automated Evaluation of HER-2/neu Immuno-histo-chemical Expression in Breast Cancer using Digital Microscopy”, in Biomedical Imaging: From Nano to Macro, pp. 808- 811, May 2008.

Hai Gao, Wan-Chi Siu, and Chao-Huan Hou, “Improved Techniques for Automatic Image Segmentation”, in IEEE Transactions on Circuits and December 2001.

P. Salembier and M. Pardas, “Hierarchical morphological segmentation for image sequence coding”, in IEEE Transactions on Image Processing, vol. 3, pp. 639–651, September 1994.

R. M. Haralik and L. G. Shapiro, “Survey: Image segmentation techniques”, in Computer Vision, Graphics and Image Processing, vol. 29, pp. 100-132, 1985.

Cheng, H.D. and Y.Sun, “A Hierarchical Approach to Color Image Segmentation Using Homogeneity”, in IEEE Transactions on Image Processing, vol. 9, no. 12, pp. 2071-2082, 2000.

Cheng, Y.Sun, “Mean Shift, Mode Seeking, and Clustering”, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no.8, pp. 790-799, 1995.

J. M. Sharif, M. F. Miswan, M. A. Ngadi, Md Sah Hj Salam,

Muhammad Mahadi bin Abdul Jamil, “Red Blood Cell Segmentation Using Masking and Watershed Algorithm: A Preliminary Study”, in 2012 International Conference on Biomedical Engineering (ICoBE), Penang, Malaysia, pp. 27-28, February 2012.

N. Malpica, C. de Solrzano, I. J.Vaquero,A. Santos, I.Vallcorba, J. Garca-Sagredo, and F. del Pozo, “Applying watershed algorithms to the segmentation of clustered nuclei”, in Cytometry, vol. 28, pp. 289–297, 1997.

J. B. T. M. Roerdink and A. Meijster, “The watershed transform: Definitions, algorithms and parallelization strategies”, in Fundamental Informatics, vol. 41, pp. 187–228, 2001.

C. Ortiz de Solorzano, E. Garcia Rodriguez, A. Jones, D. Pinkel, J.W. Gray, D. Sudar, and S. J. Lockett, “Segmentation of confocal microscope images of cell nuclei in thick tissue sections”, in Journal of Microscopy, vol. 193, no. 3, pp. 212–226, March 1999.

H. Ancin, B. Roysam, T. E. Dufresne, M. M. Chestnut, G. M. Ridder, D. H. Szarowski, and J. N. Turner, “Advances in automated 3-D image analyses of cell populations imaged by confocal microscopy”, in Cytometry, vol. 25, no. 3, pp. 221–234, November, 1996.

M. K. Chawla, G. Lin, K. Olson, A. Vazdarjanova, S. N. Burke, B. L. McNaughton, P. F.Worley, J. F. Guzowski, B. Roysam, and C. A. Barnes, “3D-catFISH: a system for automated quantitative threedimensional compartmental analysis of temporal gene transcription activity imaged by fluorescence in situ hybridization”, in Journal of Neuroscience Methods, vol. 139, no. 1, pp. 13–24, October, 2004.

G. Lin, U. Adiga, K. Olson, J. F.Guzowski, C. A. Barnes, and B. Roysam, “A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks”, in Cytometry A, vol. 56, no. 1, pp. 23–36, November

G. Lin, M. K. Chawla, K. Olson, C. A. Barnes, J. F. Guzowski, C. Bjornsson, W. Shain, and B. Roysam, “A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images”, in Cytometry A, vol. 71, no. 9, pp. 724–736, September 2007.

G. Lin, M. K. Chawla, K. Olson, J. F. Guzowski, C. A. Barnes, and B. Roysam, “Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei”, in Cytometry A, vol. 63, no. 1, pp. 20–33, 2005.

C. Wahlby, I. M. Sintorn, F. Erlandsson, G. Borgefors, and E. Bengtsson, “Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections”, in Journal of Microscopy Oxford, vol. 215, pp. 67–76, July 2004.

R. W. Mackin, B. Roysam, T. J. Holmes, and J. N. Turner, “Automated three-dimensional image analysis of thick and overlapped clusters in cytologic preparations. Application to cytologic smears”, in Analytical and Quantitative Cytology and Histology, vol. 15, no. 6, pp. 405–417, December 1993.

R.W. Mackin, Jr., L.M. Newton, J. N. Turner, and B. Roysam, “Advances in high-speed, three-dimensional imaging and automated segmentation algorithms for thick and overlapped clusters in cytologic preparations. Application to cervical smears”, in Analytical and Quantitative Cytology and Histology, vol. 20, no. 2, pp. 105–121, April 1998.


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