乳腺图像微钙化簇主动学习检测新方法

New method for microcalcification clusteres detection using active learning in the mammogram

提出一种基于主动学习的微钙化簇区域检测新算法,利用方向差分滤波器组对微钙化区域进行增强和特征提取,同时抑制高亮血管和导管等复杂区域的干扰;利用基于Bootstrap的主动学习样本方法进行样本选择和分类器训练;采用训练后的分类器实现乳腺X-线图像中钙化簇区域检测.实验结果表明,相对于被动学习的分类器检测效果,新算法在保持检出率的同时使假阳性率降低了约4.7%,取得了较好的检测效果.

A new approach to microcalcification clusters detection is proposed, based on active learning. The proposed algorithm first enhances the microcalcification region with a directional difference filter bank which effectively realizes the feature extraction and meanwhile suppresses the blood vessels and mammary duts. Then the active sample selecting method based on Bootstrap is employed to select the training set and train the Baysian classifier. Finally the obtained classifier can be used to detectmicrocalcification clusters in the mammogram. Experimental results show that the proposed algorithm achieves good performance. Compared with the traditional passive learning methods, the new algorithm reduce the false positive rate 4.7 % by keeping the same sensitivity.