用遗传算法自动提取乳腺热成像中的感兴趣区域。
ROI Extraction in Thermographic Breast Images Using Genetic Algorithms
- 结合颜色信息与心形函数设计适应度函数，通过遗传算法定位乳腺区域。
- 在58张图像中成功分割52张，准确率达90%以上。
- 无需人工选点，可提升癌症检测精度与图像采集标准化。
本文提出一种基于遗传算法（GA）的乳腺热成像中感兴趣区域（ROI）提取方法。该方法利用颜色信息及基于心形曲线的适应度函数，首次在文献中实现基于遗传算法与心形函数的ROI提取。实验表明，该方法可在58张图像中成功分离出52张的乳腺区域，具有完全自动化特性，无需手动选择种子点。该技术有助于提高乳腺癌检测的准确性，并促进热成像采集流程的标准化。
原文摘要 · Abstract (English)
This work proposes the use of Genetic Algorithms (GA) to identify the area of the breast from the background in thermographic breast images. The proposed method uses color information, a fitness function based on cardioids, and GA. This is the first work in the literature to propose a Region of Interest (ROI) extraction based on GA and cariods. ROI extraction can improve the accuracy of cancer detection and assist with the standardization of acquisition protocols. The method is able to successfully separate the breast region in 52 out of 58 images, while being fully automatic, and not requiring manual selection of seed points.
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