Mújica-Vargas Dante, Matuz-Cruz Manuel, García-Aquino Christian, Ramos-Palencia Celia
Departamento de Ciencias Computacionales, Tecnológico Nacional de México, Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca 62490, Morelos, Mexico.
Tecnológico Nacional de México, Instituto Tecnológico de Tapachula, Tapachula 30700, Chiapas, Mexico.
Entropy (Basel). 2022 Dec 5;24(12):1775. doi: 10.3390/e24121775.
In this study, a high-performing scheme is introduced to delimit benign and malignant masses in breast ultrasound images. The proposal is built upon by the Nonlocal Means filter for image quality improvement, an Intuitionistic Fuzzy C-Means local clustering algorithm for superpixel generation with high adherence to the edges, and the DBSCAN algorithm for the global clustering of those superpixels in order to delimit masses' regions. The empirical study was performed using two datasets, both with benign and malignant breast tumors. The quantitative results with respect to the BUSI dataset were JSC≥0.907, DM≥0.913, HD≥7.025, and MCR≤6.431 for benign masses and JSC≥0.897, DM≥0.900, HD≥8.666, and MCR≤8.016 for malignant ones, while the MID dataset resulted in JSC≥0.890, DM≥0.905, HD≥8.370, and MCR≤7.241 along with JSC≥0.881, DM≥0.898, HD≥8.865, and MCR≤7.808 for benign and malignant masses, respectively. These numerical results revealed that our proposal outperformed all the evaluated comparative state-of-the-art methods in mass delimitation. This is confirmed by the visual results since the segmented regions had a better edge delimitation.
在本研究中，引入了一种高性能方案来界定乳腺超声图像中的良性和恶性肿块。该方案基于非局部均值滤波器来改善图像质量，基于直觉模糊C均值局部聚类算法生成高度贴合边缘的超像素，以及基于DBSCAN算法对这些超像素进行全局聚类以界定肿块区域。实证研究使用了两个数据集，均包含良性和恶性乳腺肿瘤。对于BUSI数据集，良性肿块的定量结果为JSC≥0.907、DM≥0.913、HD≥7.025和MCR≤6.431，恶性肿块的定量结果为JSC≥0.897、DM≥0.900、HD≥8.666和MCR≤8.016；而对于MID数据集，良性肿块的定量结果为JSC≥0.890、DM≥0.905、HD≥8.370和MCR≤7.241，恶性肿块的定量结果为JSC≥0.881、DM≥0.898、HD≥8.865和MCR≤7.808。这些数值结果表明，我们的方案在肿块界定方面优于所有评估的对比先进方法。视觉结果也证实了这一点，因为分割区域具有更好的边缘界定。
