Automatic Breast Lesion Segmentation Using Continuous Max-Flow Algorithm in Phase Preserved DCE-MRIs

Abstract

In this work, we propose a framework for the automatic and accurate segmentation of breast lesions from the Dynamic Contrast Enhanced (DCE) MRI. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. The proposed method is achieved via three steps. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. The efficiency of the proposed method is verified with a series of qualitative and quantitative experiments carried out on 21 cases with two different MR image resolutions. Performance results demonstrate the quality of segmentation obtained from the proposed method.