A new image segmentation method by minimizing normalized total variation

Abstract

We present a new segmentation method based on minimizing the summation of total variations of the foreground and background of image. In this method, we define a non-parametric cost function of normalized total variation (NTV) and search over the histogram of an image for a threshold that minimizes the cost function. By its design, the method does not involve any preset or user-specified parameters, making the implementation and usage of the method consistent across sites and users. Contribution of the method is the adoption of the concept of total variation to irregular shapes that result from image segmentation. Novelty of the method is the use of total variation as a metric for assessing the appropriateness of segmentation. We applied the method to different kinds of images for evaluation and compared the method to some widely used existing methods. Visual inspection and quantitative evaluation show that the new method can achieve superior performance.