Pandey, Dinesh, Wang, Hua ORCID: 0000-0002-8465-0996, Yin, Xiaoxia, Wang, Kate ORCID: 0000-0001-5208-1090, Zhang, Yanchun ORCID: 0000-0002-5094-5980 and Shen, Jing (2022) Automatic breast lesion segmentation in phase preserved DCE-MRIs. Health Information Science and Systems, 10 (1). ISSN 2047-2501

Abstract

We offer a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. Three stages are required to complete the proposed approach. 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. A series of qualitative and quantitative trials employing nine performance metrics on 21 cases with two different MR image resolutions were used to verify the effectiveness of the proposed method. Performance results demonstrate the quality of segmentation obtained from the proposed method.

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