One of the important problems in computer-aided diagnosis of connective tissue disease is automatic recognition of staining patterns present in HEp-2 cells. In this regard, the paper introduces a novel approach for the recognition of staining patterns by HEp-2 cell indirect immunofluorescence image analysis. The proposed method assumes that a fixed set of local texture descriptors or scales may not be effective for classifying staining patterns into multiple classes. A particular set of descriptors or scales may be significant for classifying a pair of classes, but may not be relevant for other pairs of classes. The proposed approach, therefore, first selects a set of local texture descriptors under appropriate scales for each class-pair, and then forms the final feature set for multiple classes from the relevant descriptors of all possible pairs of classes. A novel framework, termed as Rough-Bayesian model, is introduced to evaluate the relevance of a descriptor and/or a scale. It is based on the merits of rough sets and Bayes decision theory. During the selection of relevant descriptor and/or scale, the proposed method takes care of the presence of both noisy pixels in an HEp-2 cell image and noisy HEp-2 cell images in a staining pattern class. The support vector machine is used to predict the staining patterns present in HEp-2 cell images. The performance of the proposed method, along with a comparison with state-of-the-art methods, is demonstrated on several HEp-2 cell image databases. An important finding is that the accuracy for classifying HEp-2 cell images is significantly increased if class-pair specific descriptors under appropriate scales are considered, instead of selecting a uniform set of descriptors and scales for multiple classes.

在计算机辅助诊断结缔组织病中的重要问题之一是自动识别HEp-2细胞中存在的染色模式。在这方面，本文介绍了一种通过HEp-2细胞间接免疫荧光图像分析识别染色模式的新方法。所提出的方法假设一组固定的局部纹理描述符或比例可能不适用于将染色图案分类为多个类别。一组特定的描述符或量表对于分类一对类别可能很重要，但可能与其他类别的类别无关。因此，所提出的方法首先为每个类对选择一组适当比例的局部纹理描述符，然后根据所有可能的类别对的相关描述符为多个类别形成最终特征集。介绍了一种新颖的框架，称为粗糙贝叶斯模型，以评估描述符和/或量表的相关性。它基于粗糙集和贝叶斯决策理论的优点。在选择相关的描述符和/或标度期间，所提出的方法要照顾到染色模式类别中HEp-2细胞图像中有噪点像素和HEp-2细胞图像中有噪点的存在。支持向量机用于预测HEp-2细胞图像中存在的染色模式。在几个HEp-2细胞图像数据库中证明了所提出方法的性能以及与最新方法的比较。