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首页> 外文会议>International conference on machine vision >Breast Mass Segmentation in Mammograms Combining Fuzzy C- Means and Active Contours
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Breast Mass Segmentation in Mammograms Combining Fuzzy C- Means and Active Contours

机译：结合模糊C均值和主动轮廓的乳房X线照片乳腺分割

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摘要

Segmentation of breast masses in mammograms is a challenging issue due to the nature of mammography and the characteristics of masses. In fact, mammographic images are poor in contrast and breast masses have various shapes and densities with fuzzy and ill-defined borders. In this paper, we propose a method based on a modified Chan-Vese active contour model for mass segmentation in mammograms. We conduct the experiment on mass Regions of Interest (ROI) extracted from the MIAS database. The proposed method consists of mainly three stages: Firstly, the ROI is preprocessed to enhance the contrast. Next, two fuzzy membership maps are generated from the preprocessed ROI based on fuzzy C-Means algorithm. These fuzzy membership maps are finally used to modify the energy of the Chan-Vese model and to perform the final segmentation. Experimental results indicate that the proposed method yields good mass segmentation results.
机译：由于乳房X线照相术的性质和肿块的特征，在乳房X线照片中对乳房肿块进行分割是一个具有挑战性的问题。实际上，乳腺X线照片的对比度很差，乳房肿块的形状和密度各不相同，边界模糊且界限不清。在本文中，我们提出了一种基于改进的Chan-Vese活动轮廓模型的方法，用于乳房X线照片中的质量分割。我们对从MIAS数据库中提取的大量感兴趣区域（ROI）进行了实验。所提出的方法主要包括三个阶段：首先，对ROI进行预处理以增强对比度。接下来，基于模糊C均值算法从预处理的ROI生成两个模糊隶属关系图。这些模糊隶属关系图最终用于修改Chan-Vese模型的能量并执行最终分割。实验结果表明，该方法取得了较好的质量分割结果。

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