Cerebral microbleeds (CMBs) in the brain are the essential indicators of critical brain disorders such as dementia and ischemic stroke. Generally, CMBs are detected manually by experts, which is an exhaustive task with limited productivity. Since CMBs have complex morphological nature, manual detection is prone to errors. This paper presents a machine learning-based automated CMB detection technique in the brain susceptibility-weighted imaging (SWI) scans based on statistical feature extraction and classification. The proposed method consists of three steps: (1) removal of the skull and extraction of the brain; (2) thresholding for the extraction of initial candidates; and (3) extracting features and applying classification models such as random forest and naïve Bayes classifiers for the detection of true positive CMBs. The proposed technique is validated on a dataset consisting of 20 subjects. The dataset is divided into training data that consist of 14 subjects with 104 microbleeds and testing data that consist of 6 subjects with 63 microbleeds. We were able to achieve 85.7% sensitivity using the random forest classifier with 4.2 false positives per CMB, and the naïve Bayes classifier achieved 90.5% sensitivity with 5.5 false positives per CMB. The proposed technique outperformed many state-of-the-art methods proposed in previous studies.

中文翻译：

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朴素贝叶斯分类器辅助磁敏感加权成像脑图像中脑微出血的自动检测。

大脑中的脑微出血（CMB）是痴呆和缺血性中风等严重脑部疾病的重要指标。一般来说，CMB 是由专家手动检测的，这是一项费力的任务，而且生产率有限。由于CMB具有复杂的形态性质，人工检测很容易出错。本文提出了一种基于统计特征提取和分类的脑磁敏感加权成像 (SWI) 扫描中基于机器学习的自动 CMB 检测技术。该方法包括三个步骤：（1）去除头骨并提取大脑；(2) 阈值化，用于提取初始候选者；(3) 提取特征并应用分类模型（例如随机森林和朴素贝叶斯分类器）来检测真阳性 CMB。所提出的技术在由 20 名受试者组成的数据集上进行了验证。该数据集分为训练数据和测试数据，训练数据由 14 名受试者组成，有 104 条微出血，测试数据由 6 名受试者组成，有 63 条微出血。使用随机森林分类器，我们能够实现 85.7% 的灵敏度，每个 CMB 出现 4.2 个误报，朴素贝叶斯分类器实现 90.5% 的灵敏度，每个 CMB 出现 5.5 个误报。所提出的技术优于先前研究中提出的许多最先进的方法。