MARKOV RANDOM-FIELD FOR TUMOR-DETECTION IN DIGITAL MAMMOGRAPHY
MARKOV RANDOM-FIELD FOR TUMOR-DETECTION IN DIGITAL MAMMOGRAPHY
复制标题
DOI：
10.1109/42.414622
复制
发表时间：
1995-09-01
影响因子：
10.6
通讯作者：
CLARK, RA
中科院分区：
文献类型：
--
作者：
LI, HD;KALLERGI, M;CLARK, RA
本文提出了一种用于数字乳腺X线摄影中肿瘤检测的方法。检测分两步进行：分割和分类。在分类中，基于一系列放射学、密度相关的特征，通过模糊二叉决策树将MRF分割的区域分类为可疑和正常。测试了一组正常（50）和异常（45）屏幕/胶片乳腺X线照片，后者包含48个活检证实，各种类型和细微的恶性肿块。通过自由响应接收器操作特征曲线来评估算法的检测准确度，该曲线示出了真阳性肿块的检测与每个图像的假阳性警报数量之间的关系，结果表明，在检测不同类型的肿块时可以达到90%的灵敏度，代价是每个图像有两个错误检测的信号，该算法在检测大小小于或等于10 mm的肿块所表现出的最小癌症方面非常成功。对于我们数据集中的16个此类病例，观察到94%的灵敏度，每个图像有1.5个假警报。还对算法参数对其灵敏度和特异性的影响进行了广泛研究，以优化临床观察员性能研究的方法。
A technique is proposed for the detection of tumors in digital mammography, Detection is performed in two steps: segmentation and classification, In segmentation, regions of interest are first extracted from the images by adaptive thresholding. A further reliable segmentation is achieved by a modified Markov random field (MRF) model-based method, In classification, the MRF segmented regions are classified into suspicious and normal by a fuzzy binary decision tree based on a series of radiographic, density-related features, A set of normal (50) and abnormal (45) screen/film mammograms were tested, The latter contained 48 biopsy proven, malignant masses of various types and subtlety, The detection accuracy of the algorithm was evaluated by means of a free response receiver operating characteristic curve which shows the relationship between the detection of true positive masses and the number of false positive alarms per image, The results indicated that a 90% sensitivity can be achieved in the detection of different types of masses at the expense of two falsely detected signals per image, The algorithm was notably successful in the detection of minimal cancers manifested by masses less than or equal to 10 mm in size, For the 16 such cases in our dataset, a 94% sensitivity was observed with 1.5 false alarms per image, An extensive study of the effects of the algorithm's parameters on its sensitivity and specificity was also performed in order to optimize the method for a clinical, observer performance study.