Clustered microcalcification classification using CC-MLO-View corresponding shape and distribution features

Shape of single microcalcifications (muCa++s) and distribution of them in a cluster are two key features for a radiologist to diagnose this abnormality appearing on mammograms into benign type or malignant type of breast cancer. These two features from two-dimensional (2-D) mammogram image from two mammographic views, cranio-caudad view (CC) and medio-lateral oblique view (MLO), are inevitable conflicted because of lack of depth information. It makes a large contradictory information of the same microcalcification cluster in different view. This paper proposes to use three-dimensional (3-D) shape and distribution features exacted from the view correspondence. To identify a 3-D position of microcalcifications, the candidate pairs in CC view and MLO view are stereo-matched based on their relative intensity and size. Occluded microcalcifications are separated by x-ray absorption property. The 3-D shape features are represented by their structural outline, spherical measurement, and thickness which are computed from Fourier descriptor of surface outline, compactness and its intensity, respectively. The distribution feature is represented by 3-D cluster size, average distance between each microcalcifications, and cluster density. There are 12 features used as input features for three-layer feed-forward backpropagation neural network classifier which is constructed dynamically and weighted be training with forty benign and forty malignant microcalcifications. The evaluated performance of the proposed method is 96 percent sensitivity and 91 percent specificity.