Wavelet based feature extraction method for breast cancer cytology images

Cancer of the breast is the most common cancer among women. Testing for detection of this cancer involves visual microscopic test of cytology samples such as Fine Needle Aspiration Cytology (FNAC). The result of analysis on this sample by Cyto-pathologist is crucial for breast cancer patient. In this paper, Complex wavelets are employed for multiscale image analysis to extract feature set for the description of Chromatin texture in the cytological diagnosis of invasive breast cancer. Finally, the obtained feature sets are used for training a k-nearest neighbor classifier so that it can classify malignant samples from benign, when given to it in the form of a feature set. The developed automatic classifier has been tested on FNAC samples of benign and malignant cases database and on an average 93.33% successful classification rate has been achieved.