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首页> 外文期刊>Academic radiology >A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images.
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A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images.

机译：基于模糊c均值（FCM）的方法，用于在动态对比增强MR图像中对乳腺病变进行计算机分割。

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RATIONALE AND OBJECTIVES: Accurate quantification of the shape and extent of breast tumors has a vital role in nearly all applications of breast magnetic resonance (MR) imaging (MRI). Specifically, tumor segmentation is a key component in the computerized assessment of likelihood of malignancy. However, manual delineation of lesions in four-dimensional MR images is labor intensive and subject to interobserver and intraobserver variations. We developed a computerized lesion segmentation method that has the advantage of being automatic, efficient, and objective. MATERIALS AND METHODS: We present a fuzzy c-means (FCM) clustering-based method for the segmentation of breast lesions in three dimensions from contrast-enhanced MR images. The proposed lesion segmentation algorithm consists of six consecutive stages: region of interest (ROI) selection by a human operator, lesion enhancement within the selected ROI, application of FCM on the enhanced ROI, binarization of the lesion membership map, connected-component labeling and object selection, and hole-filling on the selected object. We applied the algorithm to a clinical MR database consisting of 121 primary mass lesions. Manual segmentation of the lesions by an expert MR radiologist served as a reference in the evaluation of the computerized segmentation method. We also compared the proposed algorithm with a previously developed volume-growing (VG) method. RESULTS: For the 121 mass lesions in our database, 97% of lesions were segmented correctly by means of the proposed FCM-based method at an overlap threshold of 0.4, whereas 84% of lesions were correctly segmented by means of the VG method. CONCLUSION: Our proposed algorithm for breast-lesion segmentation in dynamic contrast-enhanced MRI was shown to be effective and efficient.
机译：理由和目的：准确量化乳腺肿瘤的形状和范围在几乎所有乳腺磁共振（MR）成像（MRI）应用中都至关重要。具体而言，肿瘤分割是恶性肿瘤可能性计算机评估中的关键组成部分。但是，手动在四维MR图像中划定病灶是费力的工作，并且存在观察者之间和观察者内部的差异。我们开发了一种计算机化的病变分割方法，该方法具有自动，高效和客观的优势。材料与方法：我们提出了一种基于模糊c均值（FCM）聚类的方法，用于从对比增强的MR图像中三维划分乳腺病变。提出的病变分割算法包括六个连续阶段：操作员选择感兴趣区域（ROI），所选ROI中的病变增强，FCM在增强的ROI上的应用，病变成员关系图的二值化，连接的组件标记和选择对象，并在所选对象上填充孔。我们将该算法应用于由121个原发性肿块组成的临床MR数据库。由专家的MR放射科医生对病变进行手动分割，作为评估计算机分割方法的参考。我们还将提出的算法与以前开发的体积增长（VG）方法进行了比较。结果：对于我们数据库中的121个肿块，通过建议的基于FCM的方法在0.4的重叠阈值下正确分割了97％的病变，而通过VG方法正确分割了84％的病变。结论：我们提出的动态对比增强MRI乳腺病变分割算法被证明是有效的。

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