Avuti Santhos Kumar, Bajaj Varun, Kumar Anil, Singh Girish Kumar
1PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, 482005 India.
2Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667 India.
Biomed Eng Lett. 2019 Nov 5;9(4):481-496. doi: 10.1007/s13534-019-00135-7. eCollection 2019 Nov.
Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected after excluding the pectoral muscle from mammogram images. Hence, it is very significant to identify and segment the pectoral muscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetism optimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among the charges to develop the members of a population. Here, both Kapur's and Otsu based cost functions are employed with EMO separately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimal threshold levels can be identified for the considered mammographic image. The proposed methodology is applied on all the three twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentation of the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found to be robust for variations in the pectoral muscle.
乳房X光图像主要用于检测乳腺癌。在从乳房X光图像中排除胸肌后，检测乳腺癌的阳性水平。因此，从乳房X光图像中识别和分割胸肌非常重要。在这项工作中，提出了一种基于电磁优化（EMO）技术的新的多级阈值处理方法。EMO基于电荷之间的吸引力和排斥力原理来发展种群成员。这里，基于卡普尔（Kapur）和大津（Otsu）的代价函数分别与EMO一起使用。这些标准函数在EMO算子上执行，直到获得最佳解。因此，可以为所考虑的乳房X光图像确定最佳阈值水平。所提出的方法应用于乳房X光图像分析协会数据集中所有的三张22幅乳房X光图像，并且对于大多数乳房X光图像成功实现了胸肌的分割。因此，发现所提出的算法对于胸肌的变化具有鲁棒性。
