A method for threshold selection in binary images using mean adjacent‐pixel number
Koichi Sasakawa, Shin'ichi Kuroda, Shigeki Ikebata · Systems and Computers in Japan · 1991
Abstract This paper proposes a method of selecting an appropriate adaptive threshold in binarization (0 or 1) of a gray‐level image. A measure representing compactness of a connected component in an image, “the mean adjacent‐pixel number,” is introduced. The best threshold is determined by taking the maximal of this measure. The method is applied successfully to actual gray‐level images. The results show that appropriate thresholds can be obtained even for difficult images (e.g., a small object in a noisy and low‐contrast image) which are less successful in conventional adaptive methods (e.g., the gray‐level histogram method). Conventional methods using an adaptive threshold generally have a shortcoming in that the amount of computation is proportional to the number of thresholds. This paper also proposes a method to calculate the mean adjacent‐pixel number at a high speed by combining the rank filters and histograms so that the amount of computation becomes independent of the number of thresholds.