Data from: A novel cascade classifier for automatic microcalcification detection

In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC). Our framework comprises three classification stages: i) a random forest (RF) classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target mammogram are efficiently eliminated; ii) a more complex discriminative restricted Boltzmann machine (DRBM) classifier for μC candidates determined in the RF stage, which automatically learns the detailed morphology of μC appearances for improved discriminative power; and iii) a detector to detect clusters of μCs from the individual μC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish μCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases. Experimental evaluation is conducted on the original Mammographic Image Analysis Society (MIAS) and mini-MIAS databases, as well as our own Seoul National University Bundang Hospital digital mammographic database. It is shown that the proposed method outperforms comparable methods in terms of receiver operating characteristic (ROC) and precision-recall curves for detection of individual μCs and free-response receiver operating characteristic (FROC) curve for detection of clustered μCs.

本文针对单个微钙化点与微钙化簇（microcalcifications，以下简称μC）的自动检测任务，提出一种新颖的级联分类框架。该框架包含三个分类阶段：(i) 随机森林（random forest，以下简称RF）分类器：基于捕获单个μC二阶局部结构的简单特征，高效剔除目标乳腺X线摄影图像中的非μC像素；(ii) 判别式受限玻尔兹曼机（discriminative restricted Boltzmann machine，以下简称DRBM）分类器：针对RF阶段筛选出的μC候选区域，自动学习μC表现的详细形态特征，以提升模型判别能力；(iii) 簇检测模块：基于前序单个μC的检测结果，采用两种不同准则完成微钙化簇的检测。依托该两阶段RF-DRBM分类器，本研究既可通过显式计算得到的特征区分μC，同时还能学习可进一步区分易混淆样本的隐式特征。实验评估在原始乳腺影像分析学会（Mammographic Image Analysis Society，以下简称MIAS）数据库、mini-MIAS数据库，以及本研究依托首尔大学盆唐医院构建的数字化乳腺X线摄影数据库上开展。实验结果表明，针对单个μC检测任务，所提方法在受试者工作特征（receiver operating characteristic，以下简称ROC）曲线与精确率-召回率曲线指标上均优于同类方法；针对微钙化簇检测任务，其在自由响应受试者工作特征（free-response receiver operating characteristic，以下简称FROC）曲线指标上同样优于同类方法。