CAJ | 학술논문

为了减轻传统计算机辅助检测系统中感兴趣区域标定的时间和精力，提出针对钼靶X光乳腺钙化簇检测的示例选择算法。以分块形式对图像打包，对所有负包示例进行纹理建模，并计算每个未知包中的示例与负包平均模型的距离，选取最大距离的示例作为可疑区域。实验结果表明，该算法在不需要人工标注感兴趣区域，不降低钙化簇检测性能的前提下，大幅度减少了运算时间和空间。
위료감경전통계산궤보조검측계통중감흥취구역표정적시간화정력，제출침대목파X광유선개화족검측적시례선택산법。이분괴형식대도상타포，대소유부포시례진행문리건모，병계산매개미지포중적시례여부포평균모형적거리，선취최대거리적시례작위가의구역。실험결과표명，해산법재불수요인공표주감흥취구역，불강저개화족검측성능적전제하，대폭도감소료운산시간화공간。
A novel approach based on instance selection for calcification cluster in X-ray mammography images is proposed in order to reduce the time and energy in labeling Regions of Interest(ROI)of Computer Aided Detection(CAD)system. The images are partitioned into blocks, then the texture models are established for all instances of negative packages. Afterwards, distances from the instances of unknown packages to the negative average model are calculated, and the instance having the greatest distance is selected as the suspicious region. The experimental results show that this method not only has the ability to extract the ROI automatically, but also can reduce the computation time substantially while keeping the detection performance simultaneously.