Automatic breast lesion segmentation in phase preserved DCE-MRIs

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.

A Review of Artificial Intelligence in Breast Imaging

“…Subsequently, in the second step, the segmented results from each slice are ingeniously fused together to meticulously reconstruct the original 3D volume housing the tumor, thus providing clinicians with a comprehensive representation for diagnosis and treatment planning. In a similar vein, Pandey et al [98] pioneered a fully automatic and unsupervised approach by integrating the continuous max flow (CMF) method with sophisticated noise reduction algorithms and morphological operations. Their methodological innovation represents a significant stride towards automating the process of lesion detection and segmentation in breast MRI, streamlining clinical workflows and reducing dependency on manual intervention.…”

Guest Editorial: WWWJ Special Issue of the 22th International Conference on Web Information Systems Engineering (WISE 2021)

“…Finally, 55 submissions were selected as full papers (with an acceptance rate of 24% approximately), plus 29 as short papers. The research papers cover the areas of social network data analysis, recommender systems [1], topic modeling [2], data diversity, data similarity, context-aware recommendation, prediction [3,4], big data processing [5], cloud computing, event detection [6], data mining [7], sentiment analysis, ranking in social networks, microblog data analysis, query processing [8], spatial and temporal data, graph theory and non-traditional environments [9,10]. We are honored to have several of the world's leading experts in the field join us as distinguished keynote speakers and invited speakers.…”

Fast Lung Image Segmentation Using Lightweight VAEL-Unet

“…Breast lesions were automatically and accurately segmented using max flow and min cut problems in the continuous domain over phase preserved denoised images in [19].…”