A flexible and robust threshold selection method

Despite the great prosperity and fast development of image segmentation technology, threshold selection method is still the best choice in many practical applications. State of the art threshold selection methods perform poorly in segmenting many images with different modalities, e.g. the magnetic resonance (MR) images, cell images and laser line images. Thus, it is desirable to come up with a more robust method that could segment images with different modalities with the optimum accuracy. To this end, the method should be flexible and its parameters should be adjustable for different types of images. In this paper, we propose to compute the threshold based on the slope difference distribution which is computed from the image histogram with adjustable parameters. Firstly, the pixels are clustered based on the peaks of the slope difference distribution into different pixel classes. Secondly, the threshold is selected based on the valleys of the slope difference distribution to separate the pixel classes. The robustness of this threshold selection method relies on the adjustable parameters that could be calibrated to achieve the optimum segmentation accuracy for each specific type of images. The proposed threshold selection method is tested on both the synthesized images and the real images. Experimental results show that the proposed method outperforms state of the art methods as a whole.