Abstract
The detection of a brain tumor through magnetic resonance imaging (MRI) is still challenging when the image is in low quality. Image segmentation could be done to provide a clear brain tumor area as the region of interest. In this study, we propose an improved model-based clustering approach for MRI-based image segmentation. The main contribution is the use of the adaptive neo-normal distributions in the form of a finite mixture model that could handle both symmetrical and asymmetrical patterns in an MRI image. The neo-normal mixture model (Nenomimo) also resolves the limitation of the Gaussian mixture model (GMM) and the generalized GMM (GGMM), which are limited by the short-tailed form of their distributions and their sensitivity against noise. Model estimation is done through an optimization process using the Bayesian method coupled with a Markov chain Monte Carlo (MCMC) approach, and it employs a silhouette coefficient to find the optimum number of clusters. The performance of the Nenomimo was evaluated against the GMM and the GGMM using the misclassification ratio (MCR). Finally, this study discovered that the Nenomimo provides better segmentation results for both simulated and real data sets, with an average MCR for MRI brain tumor image segmentation of less than 3%.
Highlights
We chose brain tumor detection as the topic for this study since a brain tumor is the 15th most deadly disease in Indonesia, a comparison that includes all types of cancer
Suppose N denotes the number of pixels of the magnetic resonance imaging (MRI) image and K denotes the number of the mixture component; we assume that each pixel i belongs to a single cluster that is indexed by label zi
We presented an improved method for image segmentation, namely the neo-normal mixture model (Nenomimo)
Summary
We chose brain tumor detection as the topic for this study since a brain tumor is the 15th most deadly disease in Indonesia, a comparison that includes all types of cancer. One of them is the usage of Gaussian distributions, which have symmetry and short-tailed properties They make the GMM inflexible to overcome several cases of image segmentation that tend to have diverse patterns [11]. Some of the mentioned models still apply distributions with rules of symmetry and set the mean as the cluster center; their sensitivity to noise is still high Another problem that often arises in image segmentation is the existence of different distributions in the mixture model. Motivated by a previous study by Rasmussen [10] and Deledalle et al [14], we propose the use of mixture models with adaptive distributions that can adaptively accommodate a variety of symmetrical and asymmetrical patterns Despite those facts, these distributions could handle the limitations related to the short tail form and the lack of robustness of a Gaussian distribution.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
