Automatic Diagnosis of Masses by Using Level set Segmentation and Shape Description

We present here an approach for automatic mass diagnosis in mammographic images. Our strategy contains three main steps. Firstly, region of interests containing mass and background are segmented using a level set algorithm based on region information. Secondly, the characterisation of each segmented mass is obtained using the Zernike moments for modelling its shape. The final step is the diagnosis of masses as benign or malignant lesions, which is done using the Gentleboost algorithm that also assigns a likelihood value to the final result. The experimental evaluation, performed using two different digitised databases and Receiver Operating Characteristics (ROC) analysis, proves the feasibility of our proposal, showing the benefits of a correct shape description for improving automatic mass diagnosis.