International Journal of Engineering and Technology

The number of Breast cancer has been increasing over the past three decades. Early detection of breast cancer is crucial for an effective treatment. Mammography is used for early detection and screening. Especially for young women, mammography procedures may not be very comfortable. Moreover, it involves ionizing radiation. Ultrasound is broadly popular medical imaging modality because of its non-invasive, real time, convenient and low cost nature. However, the excellence of ultrasound image is corrupted by a speckle noise. The presence of speckle noise severely degrades the signal-to noise ratio (SNR) and contrast resolution of the image. Therefore speckle noise need to be reduced before extracting the features. In this research focus on developing an algorithm to reduce the speckle noise, feature extraction and classification methods for benign and malignant tumors showed that SVM-Polynomial classification produces a high classification rate (77%) for Grey level Co-occurrence matrix (GLCM) based Contourlet features for wavelet soft thresholding denoised breast ultrasound images.