Automatic suspicions lesions segmentation based on variable-size windows in mammography images

Abstract

Breast cancer is the second main cause of death in women of western countries, so early detection and prevention is crucial. Early detection increases the likelihood of treatment as well as patient resistance. Among breast cancer detection methods, mammography is the most effective diagnostic method. For radiologists, diagnosing a cancerous mass on mammographic images is prone to error, which shows there is a need for a method to reduce the errors. In this study, a new adaptive thresholding method is proposed based on variable-sized windows. This method estimates the location of the mass and then determines the exact location of the cancerous tissue to reduce false positives. To detect the mass automatically, firstly, the histograms diagram and its relative maximums have been used to calculate the initial threshold for estimating the mass location. Two windows that contain information around each pixel and their size varies according to the mean value of each image due to the preservation of useful information. Secondly, two windows are used for the final threshold in order to discover the location of the mass and its exact shape. The proposed approach has been applied to 170 images of the Mammographic Image Analysis Society MiniMammographic database. Evaluations have shown 96.7% sensitivity and 0.79 false-positive rates, which prove an improvement in comparison with other state-of-the-art methods.