A level-set method for inhomogeneous image segmentation with application to breast thermography images

Various level-set methods have been suggested for segmenting images with intensity inhomogeneity as local region-based models. The challenge in these methods is segmenting the inhomogeneous images with smooth edges. These methods cannot properly segment regions with smooth edges in inhomogeneous images. This paper presents a new local region-based active contour model called local self-weighted active contour model. In the proposed method, a novel different weighting technique is applied. In this model, the weight of each neighbour pixel in the energy function is set by a function of its intensity and not its geometrical distance regarding the central pixel as previous methods. Considering this, the presented approach can segment regions with smooth edges in the presence of inhomogeneity as breast thermography images. The experimental results of applying the model on heterogeneous images containing synthetic images and medical images, especially breast thermography images, are compared with well-known local level-set methods which show the perfect capability of the model. The segmentation results were evaluated using the F-score, accuracy, precision and recall criteria. The results show values of 0.8, 0.62, 0.73 and 0.82 for the average accuracy, F-score, precision and recall criteria on the segmentation of breast thermography images, respectively.