所属单位：

管理学院

发表刊物：

Asia-Pacific Conference on Information Processing (APCIP 2009)

关键字：

中文关键字：clustered microcalcifications; boosting; twin supp，英文关键字：clustered microcalcifications; boosting; twin supp

摘要：

Clustered microcalcifications (MCs) are one of the early signs of breast cancer, and they are of great importance for an early diagnosis. Moreover, the spatial distribution and the shape of the microcalcifications have a significant impact in medical practice to evaluate the probability of malignancy of the tumor. In this paper we investigate an approach based on boosted twin support vector (Boosting-TWSVM) for detection of microcalcifications clusters (MCs) in digital mammograms.In the algorithm, we formulate MCs detection as a supervised-learning problem and apply the trained Boosted-TWSVM classifier to develop the detection algorithm. We tested the proposed method using DDSM database of 80cases mammograms containing about 980 MCs. Detection performance of the proposed method is evaluated by using receiver operating characteristic (ROC) curves. We compared the proposed algorithm with other existing methods. In our experiments, the proposed detection method outperformed the other methods tested. In particular, a sensitivity as high as 92.35% was achieved by our detection algorithm at an error rate of 8.3%. The experiment results suggest that Boosted-TWSVM is a promising technique for MCs detection.

备注：

张新生

第一作者：

张新生

论文类型：

期刊论文

卷号：

卷：1

期号：

期：

页面范围：

页：149-152

是否译文：

否

发表时间：

2009-07-01