The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of clinical imaging patterns into healthy and diseased states. We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of classifiers that we expect to yield more accurate numerical solutions than conventional sparse analyses of the complete spatial domain of the images. We introduce two classification decision strategies based on maximum a posteriori probability (BBMAP), or a log likelihood function (BBLL) and an approach to adjusting the classification decision criteria. To evaluate the performance of the proposed approach we used cross-validation techniques on imaging datasets with disease class labels. We first applied the proposed approach to diagnosis of osteoporosis using bone radiographs. In this problem we assume that changes in trabecular bone connectivity can be captured by intensity patterns. The second application domain is separation of breast lesions into benign and malignant categories in mammograms. The object classes in both of these applications are not linearly separable, and the classification accuracy may depend on the lesion size in the second application. Our results indicate that the proposed integrative sparse analysis addresses the ill-posedness of the approximation problem and produces very good class separation for trabecular bone characterization and for breast lesion characterization. Our approach yields higher classification rates than conventional sparse classification and previously published convolutional neural networks (CNNs) that we fine-tuned for our datasets, or utilized for feature extraction. The BBLL technique also produced higher classification rates than learners using hand-crafted texture features, and the Bag of Keypoints, which is a sophisticated patch-based method. Furthermore, our comparative experiments showed that the BBLL function may yield more accurate classification than BBMAP, because BBLL accounts for possible estimation bias.

在过去的十年中，高维空间中样本的稀疏表示这一主题引起了越来越多的兴趣。在这项工作中，我们开发了基于稀疏表示的方法，用于将临床成像模式分类为健康和疾病状态。我们提出了一种空间块分解方法来解决逼近问题的不规则性，并构建一个分类器的集合，我们希望这些分类器能够产生比图像完整空间域的传统稀疏分析更准确的数值解。我们介绍了两种基于最大后验概率 (BBMAP) 或对数似然函数 (BBLL) 的分类决策策略以及一种调整分类决策标准的方法。为了评估所提出方法的性能，我们在具有疾病类别标签的成像数据集上使用了交叉验证技术。我们首先将所提出的方法应用于使用骨片诊断骨质疏松症。在这个问题中，我们假设小梁骨连接的变化可以通过强度模式来捕捉。第二个应用领域是在乳房 X 光照片中将乳房病变分为良性和恶性类别。这两个应用程序中的对象类别都不是线性可分的，分类精度可能取决于第二个应用程序中的病变大小。我们的结果表明，所提出的综合稀疏分析解决了近似问题的不适定性，并为小梁骨表征和乳房病变表征产生了非常好的类分离。我们的方法比传统的稀疏分类和先前发布的卷积神经网络 (CNN) 产生更高的分类率，我们针对我们的数据集进行了微调，或用于特征提取。与使用手工制作的纹理特征和关键点包（一种复杂的基于补丁的方法）的学习者相比，BBLL 技术还产生了更高的分类率。此外，我们的比较实验表明，BBLL 函数可能比 BBMAP 产生更准确的分类，因为 BBLL 考虑了可能的估计偏差。与使用手工制作的纹理特征和关键点包（一种复杂的基于补丁的方法）的学习者相比，BBLL 技术还产生了更高的分类率。此外，我们的比较实验表明，BBLL 函数可能比 BBMAP 产生更准确的分类，因为 BBLL 考虑了可能的估计偏差。与使用手工制作的纹理特征和关键点包（一种复杂的基于补丁的方法）的学习者相比，BBLL 技术还产生了更高的分类率。此外，我们的比较实验表明，BBLL 函数可能比 BBMAP 产生更准确的分类，因为 BBLL 考虑了可能的估计偏差。