The two phase generalized mean model for image segmentation

Image segmentation is one of the crucial tasks in medical image processing and computer vision. The goal for image segmentation is to separate the pixels in the image into its constituent parts. Variational model in image segmentation involves formulating image segmentation as an optimization problem. The models seeking a partition of the image into meaningful regions by minimizing or maximizing an energy functional. These models often use geometric information to represent object boundaries. The models involve techniques such as active contours and level set methods. In this paper, the generalized mean model for image segmentation is investigated. The model is a 2D region-based model which utilizes the fuzzy level set method. The model is compared with the active contour without edges model also known as the Chan-Vese for three types of images: without noise, with noise and with sinusoidal intensity inhomogeneity. Based on the numerical results, the generalized mean model obtained higher accuracy and Dice similarity measure compared to the Chan-Vese model based on the tested images. The model is useful in medical imaging for disease detection, diagnosis, and treatment planning.