In this paper, an adaptive thresholding technique based on gray level co-occurrence matrix (GLCM) is presented to handle images with fuzzy boundaries. As GLCM contains information on the distribution of gray level transition frequency and edge information, it is very useful for the computation of threshold value. Here the algorithm is designed to have flexibility on the edge definition so that it can handle the object's fuzzy boundaries. By manipulating information in the GLCM, a statistical feature is derived to act as the threshold value for the image segmentation process. The proposed method is tested with the starfruit defect images. To demonstrate the ability of the proposed method, experimental results are compared with three other thresholding techniques.
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