Modified Fuzzy Linear Discriminant Analysis for Threshold Selection

Otsu’s thresholding method is a popular and efficient method for image segmentation. However, its performance is greatly affected by noise and the population size of object and background. In this paper, a novel thresholding method is proposed based on modified fuzzy linear discriminant analysis (MFLDA). MFLDA is an extension of linear discriminant analysis to fuzzy domain, where the between-class variance is modified as the distance between the centers of background and object. The optimal threshold is selected such that the MFLDA criterion is maximized. Some images are used to test the performance of the proposed thresholding method and results reveal that the proposed method is less affected by noise, the population size of objects and background, and better segmentation results are obtained than Otsu’s method and other classical thresholding methods.