ArticleHealth information science and systems2022
Automatic breast lesion segmentation in phase preserved DCE-MRIs.
Article in Health information science and systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Sustainable deep learning-based breast lesion segmentation: impact of breast region segmentation on performance.BMC medical imaging · 2025Article
- A multi-source heterogeneous medical data enhancement framework based on lakehouse.Health information science and systems · 2024Article
- Memetic ant colony optimization for multi-constrained cognitive diagnostic test construction.Health information science and systems · 2024Article
- A Review of Artificial Intelligence in Breast Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- A deep reinforcement learning-based wireless body area network offloading optimization strategy for healthcare services.Health information science and systems · 2023Article
- Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm.Health information science and systems · 2023Article
- The nnU-Net based method for automatic segmenting fetal brain tissues.Health information science and systems · 2023Article
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Authors and funding
6 authors.
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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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