Extending contour level set model for multi-class image segmentation with Application to Breast Thermography Images

Abstract

Extraction of target objects with the desired intensity value in multi-region images is a challenging task. This paper presents a new modification to the region-based level set method called Extending Contour Level Set (ECLS) model. In classical level set methods, the intensity of each pixel is compared with both average intensities inside and outside the contour; therefore, image domain is segmented into two regions with the most difference between average intensities. In multi-region images, there is no reason the selected region belongs to the desired one. In our approach, with the assumption that the initial contour is located inside the target object, outside-contour pixels are allocated to the inside-contour region if they have an intensity value close to the inside-contour average intensity. The ECLS model is compared with well-known level set methods such as the Chan-Vese model, the local binary fitting energy model, the geodesic level set model, the local statistical active contour model, and the local and global fitting image model. Simulation results indicate the remarkable advantages of the proposed method in terms of accuracy and computational complexity. To show the ability of the ECLS model in practice, a two-step approach is proposed to segment breast thermography images. In the first step, the ECLS model is exploited to produce a semi-final region. Then, in the second step, a new Controlled Chan-Vese (CCV) model is presented leading to accurate results. The CCV model controls the distance between the average intensities inside and outside the contour based on the image statistics. Comparison with other segmentation methods shows the superiority of the proposed approach. In addition, statistical measures are calculated to demonstrate the significant capabilities of the proposed method.