The week's pick
Reseach Article
A Complete Color Normalization Approach and Classification of Breast Cancer Cell
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 5 - Number 8 |
| Year of Publication: 2016 |
| Authors: Athira M. A., Aswathy M. A., Nahan Rahman |
10.5120/cae2016652344
|
Athira M. A., Aswathy M. A., Nahan Rahman . A Complete Color Normalization Approach and Classification of Breast Cancer Cell. Communications on Applied Electronics. 5, 8 ( Aug 2016), 53-58. DOI=10.5120/cae2016652344
Abstract
Breast Cancer is one of the severe diseases causing large number of deaths in women. So there is a need for efficient technique that diagnoses such cells without the involvement of human, with high accuracies. In the first phase, a complete normalization scheme is introduced to address the problem of color variation in images jointly caused by inconsistent biopsy staining and non-standard imaging condition. This scheme is robust to parameters and insensitive to content of the image and achromatic colors. Then In second phase, a Feed forward back propagation neural network classify benign and malignant tumor and also classify breast cancer tumor in type1, type2 and type3. It can be concluded that it works as promising tool for classification of cancer cells. Breast Cancer is one of the severe disease causing large number of deaths in women. So there is a need for efficient technique that diagnoses such cells without the involvement of human, with high accuracies. In the first phase, a complete normalization scheme is introduced to address the problem of color variation in images jointly caused by inconsistent biopsy staining and non-standard imaging condition. This scheme is robust to parameters and insensitive to content of the image and achromatic colors. Then In second phase, a Feed forward back propagation neural network classify benign and malignant tumor and also classify breast cancer tumor in type1, type2 and type3. It can be concluded that it works as promising tool for classification of cancer cells.
References
- Xingyu Li, Student Member, IEEE, and Konstantinos N. Plataniotis, Fellow, IEEE,”A Complete Color Normalization Approach to Histo-pathology Images Using Color Cues Computed From SaturationWeighted Statistics”,IEEE Transactions on Biomedical Engineering
- K. Jafari-Khouzani and H. Soltanian-Zadeh, Multiwavelet grading ofpathological images of prostate, IEEE Trans. Biomed. Eng., vol. 50, no. 6, pp. 697 704, Jun. 2003.
- A. Basavanhally et al., Computerized image-based detection and grading of lymphocytic infiltration in her2+ breast cancer histopathology, IEEE Trans. Biomed. Eng., vol. 57, no. 3, pp. 642 653, Mar. 2010
- K. Nguyen et al., Structure and context in prostatic gland segmentation and classification, in Proc. MICCAI, 2012.
- L. Gorelick et al., Prostate histopathology: Learning tissue component histograms for cancer detection and classification, IEEE Trans. Med. Imag., vol. 32, no. 10, pp. 1804 1818, Oct. 2013.
- M. Veta et al., Breast cancer histopathology image analysis: A revier, IEEE Trans. Biomed. Eng., vol. 61, no. 5, pp. 1400 1411, May 2014.
- M.N. Gurcan et al., Histopathological image analysis: a review, IEEE Rev. Biomed. Eng., vol. 2, pp. 147 171, 2009.
- M. Macenko et al., A method for normalizing histology slides for quantitative analysis, in Proc. ISBI, 2009.
- A. Tabesh et al., Multifeature prostate cancer diagnosis and gleason grading of histological images, IEEE Trans. Med. Imag., vol. 26, no. 10, pp. 1366 1378, Oct. 2007.
- S. Kothari et al., Automatic batch-invariant color segmentation of histological cancer images, in Proc. ISBI, 2011.
- Y. Wang et al., A color-based approach for automated segmentation in tumor tissue classification, in Proc. EMBC, 2007.
- D. Magee et al., Color normalization in digital histopathology images, in Proc. MICCAI OPTMHisE, 2009.
- A.M. Khan et al., A non-linear mapping approach to stain normalization in digital histopathology images using image-specic color deconvolution, IEEE Trans. Biomed. Eng., vol. 61, no. 6, pp. 1729 1738, Jun. 2014.
- S. Tani et al., Color standardization method and system for whole slide imaging based on spectral sensing, Analytical Cellular Pathology, vol. 35, no. 2, pp. 107 115, 2012.
- A. Basavanhally and A. Madabhushi, Em-based segmentatio-driven color standardization of digitized histopathology, in Proc. SPIE Medical Imaging 2013: Digital Pathology, 2013, vol. 8676
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