arXiv:cs/0602044v1 [cs.CV] 12 Feb 2006 Multilevel Thresholding for Image Segmentation through a Fast Statistical Recursive Algorithm S. Arora a , J. Acharya b , A. Verma c , Prasanta K. Panigrahi c1 a Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, 382 009, India b Indian Institute of Technology, Kharagpur, 721 302, India c Physical Research Laboratory, Navrangpura, Ahmedabad, 380 009, India Abstract A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance. Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a new sub-range for the next step, until no significant improvement in image quality can be achieved. The method makes use of the fact that a number of distributions tend towards Dirac delta function, peaking at the mean, in the limiting condition of vanishing variance. The procedure naturally provides for variable size segmentation with bigger blocks near the extreme pixel values and finer divisions around the mean or other chosen value for better visualization. Experiments on a variety of images show that the new algorithm effectively segments the image in computationally very less time. Key words: Multilevel Thresholding; Image Segmentation; Histogram; Recursion; Sub-range 1 Introduction Thresholding is an important technique for image segmentation. Because the segmented image obtained from thresholding has the advantage of smaller storage space, fast processing speed and ease in manipulation, compared with a gray level image containing 256 levels, thresholding techniques have drawn a lot of attention during the last few years. The aim of an effective segmentation 1 E-mail: prasanta@prl.res.in Preprint submitted to Elsevier Science 1 February 2008