Human Epithelial type 2 (HEp-2) images are important in detecting the antinuclear
autoantibody (ANA) in diagnosis of autoimmune disease in human body. Generally,
HEp-2 cells can be classified into six main patterns, namely Centromere, Nucleolar,
Homogeneous, Cytoplasmic, Fine Speckled and Coarse Speckled. However, in current
technology, HEp-2 images can only be analysed manually by indirect
immunofluorescence (IIF) test. The result of IIF test has very high variability and very
dependent on the experience of physicists. Therefore, digitalize the IIF test becomes the
new interest to researchers as well as in this research, where segmentation and features
extraction of HEp-2 images will be focused. In segmentation of HEp-2 images, the
current state-of-the-art techniques failed to provide a satisfied segmented result.
Therefore, a combination of two conventional methods (i.e. Fuzzy C-Means (FCM)
clustering and thresholding) has been proposed in this study. From the result, the
segmented images are smoother, more consistent and with lesser noises compared to
other state-of-the-art methods. In feature extraction stage, this study proposes to extract
five features, which are Contrast, Energy, Correlation, Homogeneity, and Entropy. Based
on the results obtained, the five proposed features can successfully differentiate the
staining patterns of HEp-2 cells. In short, the proposed methods in this research have
high capability to be introduced in hospital for detection of HEp-2 images for
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autoimmune disease. The proposed method has been proven with higher accuracy which
can reduce the shortcoming of the existing IIF test
