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Microscopic Image Processing Of Automated Detection And Classification For Human Cancer Cell

Journal: International Journal of Scientific & Technology Research (Vol.5, No. 9)

Publication Date:

Authors : ; ; ;

Page : 231-236

Keywords : Digital Image Processing; Breast Cancer Cells; Contrast Enhancement; K-Means Clustering; Fuzzy C-Means; Watershed; Features Extraction; Classification; SVM; K-NN; Back Propagation Neural Networks.;

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Automated Detection for Human Cancer Cell is one of the most effective applications of image processing and has obtained great attention in latest years therefore. In this study we propose an automated detection system for human cancer cells based on breast cancer cells. This study was conducted on a set of Fine Needle Aspiration FNA biopsy microscopic images that have been obtained from the Pathology Center - Faculty of Medicine - Mansoura University Hospital - Egypt is made up of 72 microscope image samples of benign 72 microscope image samples of malignant. The purpose of this study is to detect and classify the benign and malignant cells in the breast biopsy. The images are exposed to a series of pre-processing steps which include resizing image such as 10241024 512512 enhance images by remove noise through Median Filter and contrast enhancement through Unsharp Masking Adjust Intensity. The system depends on breast cancer cells detection using clustering-based segmentation K-means clustering Fuzzy C-means clustering and region-based segmentation Watershed. Shape Texture and Color features are extracted for Detection. The results show high Detection Rate for breast cancer cells images either Benign or Malignant. Finally classification stage by using Support Vector Machine K-Nearest Neighbors and Back-Propagation Neural Networks. The final classification with the best accuracy in SVM is 97.22 in K-NN and BPNNs is 98.61.

Last modified: 2017-06-11 22:52:07