Gaussian mixture model based probabilistic modeling of images for medical image segmentation

Riaz, Farhan, Rehman, Saad, Ajmal, Muhammad, Hafiz, Rehan, Hassan, Ali, Aljohani, Naif, Nawaz, Raheel, Young, Rupert and Coimbra, Miguel (2020) Gaussian mixture model based probabilistic modeling of images for medical image segmentation. IEEE Access. ISSN 2169-3536

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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. INDEX TERMS Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineering

Item Type: Article
Keywords: Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineering
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Industrial Informatics and Signal Processing Research Group
Subjects: Q Science > Q Science (General) > Q0179.9 Research
T Technology
T Technology > T Technology (General)
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Depositing User: Rupert Young
Date Deposited: 22 Jan 2020 08:20
Last Modified: 10 Feb 2020 08:15
URI: http://sro.sussex.ac.uk/id/eprint/89456

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