一种基于空间信息的改进的HEp-2细胞图像粗模糊聚类算法

Shaswati Roy, P. Maji
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引用次数: 8

摘要

间接免疫荧光(IIF)分析是抗核自身抗体(ANAs)分析中最有效的方法,可用于揭示结缔组织疾病等自身免疫性疾病的发生。在抗核抗体试验中,人上皮细胞2型(HEp-2)细胞多被用作底物。然而,识别IIF图像中ANAs的染色模式需要对感兴趣的区域进行适当的检测。在这方面,IIF图像的自动分割是必不可少的先决条件,因为人工分割是劳动密集、耗时且主观的。近年来,粗糙模糊聚类通过处理图像中存在的不同不确定性,在图像分割方面取得了显著的效果。但是,现有的鲁棒粗糙模糊聚类算法没有考虑图像的空间分布。当图像被噪声和其他伪影扭曲时,这是有用的。为此,本文提出了一种结合空间约束和鲁棒粗模糊聚类优点的分割算法。在目前的研究中,像素的类标号受到其相邻像素的空间距离的影响。这样,可以将更多的相邻像素合并到像素特征的计算中。在多幅HEp-2细胞图像上评价了该方法的性能,并与现有算法进行了定性和定量对比。
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A modified rough-fuzzy clustering algorithm with spatial information for HEp-2 cell image segmentation
Indirect immunofluorescence (IIF) analysis is the most effective test for antinuclear autoantibodies (ANAs) analysis, in order to reveal the occurrence of some autoimmune diseases, such as connective tissue disorders. In the tests of antinuclear antibodies, the human epithelial type 2 (HEp-2) cells is mostly used as substrate. However, the recognition of the staining pattern of ANAs in the IIF image requires proper detection of the region of interest. In this regard, automatic segmentation of IIF images is an essential prerequisite as manual segmentation is labor intensive, time consuming, and subjective. Recently, rough-fuzzy clustering has been shown to provide significant results for image segmentation by handling different uncertainties present in the images. But, the existing robust rough-fuzzy clustering algorithm does not consider spatial distribution of the image. This is useful when the image is distorted by noise and other artifacts. In this regard, the paper proposes a segmentation algorithm by incorporating the spatial constraint with the advantages of robust rough-fuzzy clustering. In the current study, class label of a pixel is influenced by its neighboring pixels depending on their spatial distance. In this way, more number of neighboring pixels can be incorporated into the calculation of a pixel feature. The performance of the proposed method is evaluated on several HEp-2 cell images and compared with the existing algorithms by presenting both qualitative and quantitative results.
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