Abstract
In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art.
Highlights
Image segmentation is a non-trivial task in computer vision and medical image analysis
The gradient descent algorithm is used to evolve φ to the boundaries of the object that has to be segmented by optimizing an energy functional: J = γ R(φ) + Eext (φ) where R(φ) is the regularization term for level sets and Eext (φ) is the external energy term that is dependent on the data of interest
The variational level sets framework is extended to incorporate a novel term which maximizes the distance between the object and background distributions using the Bhattacharya distance
Summary
Image segmentation is a non-trivial task in computer vision and medical image analysis. In spite of significant advances in various methods that have been developed for image segmentation, achieving better segmentation results remains a significant challenge in medical imaging. This is mainly because most intended applications for image segmentation such as surveillance, object segmentation, etc. Where R(φ) is the regularization term for level sets and Eext (φ) is the external energy term that is dependent on the data of interest. For an image segmentation application, the external energy term is derived from the image data. The external energy term Eext is defined such that it achieves a minimum when the zero level set contour φ is located at the desired position.
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