The autoimmune disorders such as rheumatoid, arthritis, and scleroderma are connective tissue diseases (CTD). Autoimmune diseases are generally diagnosed using the antinuclear antibody (ANA) blood test. This test uses indirect immune fluorescence (IIf) image analysis to detect the presence of liquid substance antibodies at intervals the blood, which is responsible for CTDs. Typically human alveolar epithelial cells type 2 (HEp2) are utilized as the substrate for the microscope slides. The various fluorescence antibody patterns on HEp-2 cells permits the differential designation-diagnosis. The segmentation of HEp-2 cells of IIf images is therefore a crucial step in the ANA test. However, not only this task is extremely challenging, but physicians also often have a considerable number of IIf images to examine.In this study, we propose a new methodology for HEp2 segmentation from IIf images by maximum modified quantum entropy. Besides, we have used a new criterion with a flexible representation of the quantum image(FRQI). The proposed methodology determines the optimum threshold based on the quantum entropy measure, by maximizing the measure of class separability for the obtained classes over all the gray levels. We tested the suggested algorithm over all images of the MIVIA HEp 2 image data set.To objectively assess the proposed methodology, segmentation accuracy (SA), Jaccard similarity (JS), the F1-measure,the Matthews correlation coefficient(MCC), and the peak signal-to-noise ratio (PSNR) were used to evaluate performance. We have compared the proposed methodology with quantum entropy, Kapur and Otsu algorithms, respectively.The results show that the proposed algorithm is better than quantum entropy and Kapur methods. In addition, it overcomes the limitations of the Otsu method concerning the images which has positive skew histogram.This study can contribute to create a computer-aided decision (CAD) framework for the diagnosis of immune system diseases

类风湿，关节炎和硬皮病等自身免疫性疾病是结缔组织疾病（CTD）。通常使用抗核抗体（ANA）血液测试来诊断自身免疫性疾病。该测试使用间接免疫荧光（IIf）图像分析来检测间隔一定时间的血液中是否存在液态物质抗体，这是造成CTD的原因。通常，将人2型肺泡上皮细胞（HEp2）用作显微镜载玻片的底物。HEp-2细胞上的各种荧光抗体图谱可以进行差异性的名称诊断。因此，IIf图像的HEp-2细胞的分割是ANA测试中的关键步骤。但是，不仅这项任务非常艰巨，而且医师通常还需要检查大量的IIf图像。在这项研究中，我们提出了一种通过最大修正量子熵从IIf图像分割HEp2的新方法。此外，我们使用了新的标准，可以灵活地表示量子图像（FRQI）。所提出的方法根据量子熵测度确定最佳阈值，方法是在所有灰度级上最大化所获得类别的类别可分离性的度量。我们在MIVIA HEp 2图像数据集的所有图像上测试了建议的算法。峰值信噪比（PSNR）用于评估性能。我们将拟议的方法分别与量子熵，Kapur和Otsu算法进行了比较。结果表明，该算法优于量子熵和Kapur方法。此外，它克服了Otsu方法在直方图为正的图像方面的局限性。这项研究可为创建用于免疫系统疾病诊断的计算机辅助决策（CAD）框架做出贡献