Université de Tunis El Manar, Ecole Nationale d'Ingnieurs de Tunis, LR-Signal Image et Technologies de l'Information, Tunis 1002, Tunisie; IMT Atlantique, ITI Laboratory, Brest 29238, France.
Université de Tunis El Manar, Ecole Nationale d'Ingnieurs de Tunis, LR-Signal Image et Technologies de l'Information, Tunis 1002, Tunisie.
Comput Methods Programs Biomed. 2018 Oct;164:131-142. doi: 10.1016/j.cmpb.2018.07.005. Epub 2018 Jul 18.
Accurate mass segmentation in mammographic images is a critical requirement for computer-aided diagnosis systems since it allows accurate feature extraction and thus improves classification precision.
In this paper, a novel automatic breast mass segmentation approach is presented. This approach consists of mainly three stages: contour initialization applied to a given region of interest; construction of fuzzy contours and estimation of fuzzy membership maps of different classes in the considered image; integration of these maps in the Chan-Vese model to get a fuzzy-energy based model that is used for final delineation of mass.
The proposed approach is evaluated using mass regions of interest extracted from the mini-MIAS database. The experimental results show that the proposed method achieves an average true positive rate of 91.12% with a precision of 88.08%.
The achieved results show high accuracy in breast mass segmentation when compared to manually annotated ground truth and to other methods from the literature.
在乳腺 X 线图像中进行精确的质量分割是计算机辅助诊断系统的关键要求，因为它允许进行准确的特征提取，从而提高分类精度。
本文提出了一种新的自动乳腺肿块分割方法。该方法主要包括三个阶段：应用于给定感兴趣区域的轮廓初始化；在考虑的图像中构建模糊轮廓并估计不同类别的模糊隶属度图；将这些地图集成到 Chan-Vese 模型中，得到一个基于模糊能量的模型，用于最终勾勒出肿块。
该方法使用从 mini-MIAS 数据库中提取的肿块感兴趣区域进行评估。实验结果表明，该方法的平均真阳性率为 91.12%，精度为 88.08%。
与手动标注的地面实况以及文献中的其他方法相比，所得到的结果表明在乳腺肿块分割中具有很高的准确性。
