An artificial intelligent algorithm for tumor detection in screening mammogram
An artificial intelligent algorithm for tumor detection in screening mammogram
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DOI：
10.1109/42.932741
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发表时间：
2001-07
影响因子：
10.6
通讯作者：
Lei Zheng;Andrew K. Chan
中科院分区：
文献类型：
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作者：
Lei Zheng;Andrew K. Chan
癌性肿块是乳腺癌的主要类型之一。当癌性肿块嵌入并被不同密度的实质组织结构包裹时，它们很难在乳房X线照片上被视觉检测到。本文提出了一种算法，结合了几种人工智能技术与离散小波变换（DWT）的检测肿块的乳房X线照片。人工智能技术包括分形维数分析、多分辨率马尔可夫随机场、狗兔算法等。分形维数分析作为一个预处理器，以确定在乳房X线照片中的可疑癌症的区域的近似位置。狗和兔子的聚类算法被用来启动分割在LL子带的三级小波分解的乳房X线照片。最后应用树型分类策略来确定给定区域是否可疑为癌症。作者已经验证了该算法与322乳房X线照片在乳房X线图像分析协会数据库。验证结果表明，该算法的灵敏度为97.3%，每幅图像的假阳性数为3.92。
Cancerous tumor mass is one of the major types of breast cancer. When cancerous masses are embedded in and camouflaged by varying densities of parenchymal tissue structures, they are very difficult to be visually detected on mammograms. This paper presents an algorithm that combines several artificial intelligent techniques with the discrete wavelet transform (DWT) for detection of masses in mammograms. The AI techniques include fractal dimension analysis, multiresolution Markov random field, dogs-and-rabbits algorithm, and others. The fractal dimension analysis serves as a preprocessor to determine the approximate locations of the regions suspicious for cancer in the mammogram. The dogs-and-rabbits clustering algorithm is used to initiate the segmentation at the LL subband of a three-level DWT decomposition of the mammogram. A tree-type classification strategy is applied at the end to determine whether a given region is suspicious for cancer. The authors have verified the algorithm with 322 mammograms in the Mammographic Image Analysis Society Database. The verification results show that the proposed algorithm has a sensitivity of 97.3% and the number of false positives per image is 3.92.