Zyout Imad, Abdel-Qader Ikhlas, Jacobs Christina
Department of Electrical and Computer Engineering, Western Michigan University, MI 49008, USA.
Int J Biomed Imaging. 2009;2009:767805. doi: 10.1155/2009/767805. Epub 2010 Jan 4.
Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spectral, and statistical features of each input mammogram and generates models of real MCs to be used as training samples through a simplified learning phase of the Bayesian classifier. Followed by an estimation of the classifier's decision function parameters, a mammogram is segmented into the identified targets (MCs) against background (healthy tissue). The proposed algorithm has been tested using 23 mammograms from the mini-MIAS database. Experimental results achieved MCs detection with average true positive (sensitivity) and false positive (specificity) of 91.3% and 98.6%, respectively. Results also indicate that the modeling of the real MCs plays a significant role in the performance of the classifier and thus should be given further investigation.
乳腺钼靶片中簇状微钙化（MCs）的检测是成功检测乳腺癌的重要一步，因为其存在是癌症的早期迹象之一。本文提出了一种新框架，该框架将贝叶斯分类器与用于检测微钙化簇的模式合成方案相结合。这项工作提取每个输入乳腺钼靶片的纹理、光谱和统计特征，并通过贝叶斯分类器的简化学习阶段生成真实MCs的模型用作训练样本。在估计分类器的决策函数参数之后，将乳腺钼靶片分割为识别出的目标（MCs）和背景（健康组织）。所提出的算法已使用来自mini-MIAS数据库的23幅乳腺钼靶片进行了测试。实验结果实现了MCs检测，平均真阳性（灵敏度）和假阳性（特异性）分别为91.3%和98.6%。结果还表明，真实MCs的建模在分类器性能中起着重要作用，因此应进一步研究。
