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Cancer Cells Detection and Classification in Biopsy Image
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 38 - Number 3 |
| Year of Publication: 2012 |
| Authors: Shekhar singh |
10.5120/4667-6771
|
Shekhar singh . Cancer Cells Detection and Classification in Biopsy Image. International Journal of Computer Applications. 38, 3 ( January 2012), 15-21. DOI=10.5120/4667-6771
Abstract
In this research work, to understand the types of cancer cell and attempt to analyses the biopsy slides. In this method to identify cancer parts just using simple technique of isolation of insignificant portion of biopsy slide by cancer cell level and object level segmentation and classification. Many features used in the cancer cell detection and classification of biopsy image are inspired by clinical pathologists as important for diagnosis, prognosis and characterization. A large majority of these features are features of cell nuclei in biopsy image; as such, there is often the desire to segment the image into individual cell nuclei and cancer object. In this paper, present an analysis of the utility of color Thresholding, adaptive Thresholding and watershed method for segmentation of cancer cell nuclei for classification of H&E stained histopathology image of breast tissue using neural network. This paper showing the cell level and object level classification performance using these segmented nuclei in a benign versus malignant. Results indicate that very good segmentation and classification accuracies can be achieved with color Thresholding, adaptive Thresholding, watershed based segmentation of cancer cell nuclei and cancer objects and classification of biopsy image.
References
- Ajay Nagesh Baravanhalty, Shridar Gancean, Shannan Agner, James Peter Monaco, “Coputerised image based detection and grading of lymphocytic infiltration in HER2 breast cancer histopathology”,IEEE Transactions on biomedical engineering vol 57,no-3 march -2010.
- Jean Roman Dalle, Wee Kheng Leow, Daniel Recoeanis, Adima Eunice Tatac, Thomas C.Putti, “Automatic breast cancer grading of histopathological images”, 30th annual international IEEE EMBS conference van couver British Columbia, Canada, august , 2008.
- Ali H. Al-Timemy, Fawzi M. Al-Naima ,Nebras H. Qaeeb, “Probabilistic Neural Network for Breast Biopsy Classification”, MASAUM Journal of Computing, Volume 1 Issue 2, September 2009 .
- F. Schnorrenberg , C.S. Pattichis , K. Kyriacou , M.Vassiliou , C.N. Schizas , “Computer–aided classification of breast cancer nuclei” , Accepted for publication in the journal Technology & HealthCare, Elsevier Science B.V.,Amsterdam, Netherlands, 1996.
- C. Demir and B.Yener, “Automated cancer diagnosis based on histopathological images: a systematic survey,” Rensselaer Polytechnic Institute, Tech. Rep., 2005.
- A. Tutac, D. Racoceanu, T. Putti, W. Xong, W.-K. Leow, and V. Cretu, “Knowledge-guided semantic indexing of breast cancer histopathology images,” in Proc. Int. Conf. on Biomedical Engineering and Informatics, 2008.
- S. Petushi, F. U. Garcia, M. M. Haber, C. Katsinis, and A. Tozeren, “Large-scale computations on histology images reveal gradedifferentiating parameters for breast cancer,” BMC Medical Imaging, vol. 6, no. 14, 2006.
- S. Doyle, M. Hwang, M. Feldman, and J. Tomaszeweski, “Automated grading of prostate cancer using architectural and textural image features,” in Proc. of 4th IEEE Int. Symp. on Biomedical Imaging, 2007, pp. 1284 – 1287.
- H. Soltanian-Zadeh and K. Jafari-Khouzani, “Multiwavelet grading of prostate pathological images,” IEEE Trans. on Biomedical Engineering, vol. 50, pp. 697–704, 2003.
- A. Nedzved, S. Ablameyko, and I.Pitas, “Morphological segmentation of histology cell images,” in Proc. Int. Conf. Pattern Recognition, 2000, pp. 1500–1503.
- H. Jeong, T.-Y. Kim, H.-G. Hwang, and H.-J. Choi, “Comparison of thresholding methods for breast tumor cell segmentation,” in Proc. of 7th Int. Workshop on Enterprise networking and Computing in Healthcare Industry, 2005, pp. 392–395.
- V. Mallapragada, N. Sarkar, and T.K. Podder, “A Robotic System for Real-time Tumor Manipulation During Image guided Breast Biopsy”, IEEE International Conference on Bioinformatics and Bioengineering, October 14-17, 2007, Boston, MA, pp. 204-210.
- V. Mallapragada, N. Sarkar, and T.K. Podder, “Robot-Assisted Real-Time Tumor Manipulation for Breast Biopsy”, IEEE Transactions on Robotics, Vol. 25, Issue 2, April 2009, pp. 316-324.
- Cigdem Gunduz, Bulent Yener, and S. Humayun Gultekin, “The cell graphs of cancer”, Bioinformatics, Oxford University Press, Vol 20, Issue 1, January, 2004, pp. 145-151.
- C. Cagatay Bilgin, Cigdem Demir, Chandandeep Nagi, and Bulent Yener, “Cell-Graph Mining for Breast Tissue Modelling and Classification”, 29th IEEE EMBS Annual International Conference, August 23-26, 2007, Lyon, France.
- A.M. Tang, D.F. Kacher, E.Y. Lam, K.K.Wong, F.A. Jolesz, and E.S Yang, “Simultaneous Ultrasound and MRI System for Breast Biopsy:Compatibility Assessment and Demonstration in a Dual Modality Phantom”, IEEE Transactions on Medical Imaging, Vol. 27, Issue 2,February 2008, Davis, CA, USA, pp. 247-254.
- C. Zhu, E.S. Burnside, G.A. Sisney, L.R. Salkowski, J.M. Harter, B.Yu, and N. Ramanujam, “Fluorescence Spectroscopy: An Adjunct Diagnostic Tool to Image-Guided Core Needle Biopsy of the Breast”, IEEE Transactions on Biomedical Engineering, Vol. 56, Issue 10, October 2009, pp. 2518 – 2528.
- Tao Shi, David Seligson, Arie S Belldegrun, Aarno Palotie and Steve Horvath,” Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma”, Modern Pathology (2005) 18, 547–557.
- Anthony McCabe, Marisa Dolled-Filhart, Robert L. Camp,David L. Rimm,” Automated Quantitative Analysis (AQUA) of In Situ Protein Expression, Antibody Concentration,and Prognosis”, Journal of the National Cancer Institute, Vol. 97, No. 24, December 21, 2005.
- James W. Bacus,2 Charles W. Boone, James V. Bacus,Michele Follen, Gary J. Kelloff, Valery Kagan, and Scott M. Lippman,” Image Morphometric Nuclear Grading of Intraepithelial Neoplastic Lesions with Applications to Cancer Chemoprevention Trials”, Cancer Epidemiology, Biomarkers & Prevention Vol. 8, 1087–1094, December 1999.
- Schnorrenberg, C.S. Pattichis, K. Kyriacou, M.Vassiliou, C.N. Schizas, “Computer–aided classification of breast cancer nuclei”, accepted for publication in the journal Technology & HealthCare, Elsevier Science B.V., Amsterdam, Netherlands, 1996.
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