Complex wavelet based texture features of cancer cytology images

Breast cancer is the most frequently diagnosed cancer and the most common cause of cancer death among women. In developing countries, testing for detection of this cancer involves visual microscopic test of cytology samples such as Fine Needle Aspiration Cytology (FNAC) taken from the patient's breast. The result of analysis on this sample by Cyto-pathologist is crucial for breast cancer patient. The wavelet transform has become an important tool for many biomedical image and signal application, which at the detection and analysis of image features. In this paper, clusters of nucleus in the sub-band images of FNAC samples is investigated after decomposition by means of the Complex Discrete Wavelet Transform (CDWT) and a new method is developed to compute the variability and other nucleus statistical textural features. The experimental data are taken from FNAC samples of benign and malignant cases. The calculated variance parameters and texture features permit the characterization of nucleus clusters variable and distinguish malignant samples from benign in cytological breast fine needle aspirate images.