Classification of breast ultrasound images based on posterior feature

Ultrasonography (USG) check-up is a common way for breast cancer screening, but the result is highly subjective on the operator. Therefore, a system capable to objectively diagnose breast cancer is necessary. One of the features of breast cancer is posterior acoustic patterns. It categorized into four classes which are enhancement, shadowing, combined pattern, and no posterior acoustic feature. This paper proposes a scheme by extracting area suspected to have posterior acoustic features and background features. The dataset consists of 98 breast USG images which are classified into 69 posterior acoustic enhancement cases and 29 no posterior acoustic cases. Firstly, a pre-processing of breast USG images is conducted to eliminate speckle noise, marker, and label. Secondly, segmentation is using region growing method, and followed by extracting posterior area and its background. Feature extraction is conducted on both of areas using histogram method. Finally, classification is using Multilayer Perceptron (MLP). Performance of the proposed method successfully achieves accuracy of 87.79%, sensitivity of 92.75% and specificity of 82.75% using six histogram features. It shows that this method is successful in classifying the breast USG images. Therefore, it has potential to be implemented in an automated breast computer aided diagnosis (CAD) system.