基于NSGA-II多目标聚类的DCE-MRI乳腺病变分割

Tapas Si, D. Patra, Sukumar Mondal, Prakash Mukherjee
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引用次数: 0

摘要

在所有类型的妇女癌症中,乳腺癌造成的死亡率最高。早期发现和诊断导致早期治疗可以挽救生命。乳腺动态对比增强磁共振成像(DCE-MRI)分割的计算机辅助方法可以帮助放射科医生/医生诊断疾病并制定进一步的治疗计划。在本文中,我们提出了一种使用硬聚类技术和非主导排序遗传算法(NSGA-II)的乳腺DCE-MRI分割方法。在NSGA-II算法中,采用了众所周知的聚类有效性指标DB-index和Dunn-index作为目标函数。MRI中的噪声和强度不均匀性在预处理步骤中从MRI中去除,因为这些伪影会影响分割过程。分割后,将病灶分离,最终在MRI上定位。所设计的方法应用于乳腺10节段矢状t2加权脂肪抑制dce mri。与K-means算法进行了比较研究,所设计的方法在数量和质量上都优于K-means算法。
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Breast Lesion Segmentation in DCE-MRI using Multi-Objective Clustering with NSGA-II
Breast cancer causes the highest death among all types of cancers in women. Early detection and diagnosis leading to early treatment can save the life. The computer-assisted methodologies for breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) segmentation can help the radiologists/doctors in the diagnosis of the disease as well as further treatment planning. In this article, we propose a breast DCE-MRI segmentation method using a hard-clustering technique with a Non-dominated Sorting Genetic Algorithm (NSGA-II). The well-known cluster validity metrics namely DB-index and Dunn-index are utilized as objective functions in NSGA-II algorithm. The noise and intensity inhomogeneities in MRI are removed from MRI in the preprocessing step as these artifacts affect the segmentation process. After segmentation, the lesions are separated and finally, localized in the MRI. The devised method is applied to segment 10 Sagittal T2-Weighted fat-suppressed DCE-MRI of the breast. A comparative study has been conducted with the K-means algorithm and the devised method outperforms K-means both quantitatively and qualitatively.
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