Abstract
Many real-time engineering applications have used histogram thresholding methods that failed to segment images whose histogram had only one peak. A fuzzy c-means cluster algorithm (FCM), in contrast, can segment this type of image but at the cost of time. To improve unsupervised segmentation, the authors developed a new method for fast and efficient segmentation based on automatic histogram analysis of acquired images and a combined FCM and intensity transformation (HIST_FCM_IT) approach. The first part of the algorithm uses parabolic approximation for peak evaluation, and the second modifies image intensity to allow the partition matrix to be rapidly constant.
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