ABSTRACT Image binarization is the process of separation of pixel values into two groups, black as background and white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This paper describes a locally adaptive thresholding technique that removes background by using local mean and standard deviation. Most common and simplest approach to segment an image is using thresholding. In this work we present an efficient implementation for threshoding and give a detailed comparison of Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding algorithm is implemented on medical images. The quality of segmented image is measured by statistical parameters: Jaccard Similarity Coefficient, Peak Signal to Noise Ratio (PSNR).