###### 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%. MRI; image segmentation; neo-normal; mixture model; Bayesian; MCMC +
Footnote †: journal: Applied  
the region of interest--ROI) from the healthy brain. This helps medical surgery by providing a clear boundary to the parts of the tumor area that must be taken away without damaging healthy parts of the brain. Various image segmentation techniques have been proposed in the form of clustering algorithms. Traditional methods include hierarchical clustering and the partitioning algorithm [2]. In hierarchical clustering, objects are grouped by forming a cluster tree or dendrogram, while partitioning algorithms are based on objective functions that are optimized to obtain clustering and cluster centers. These methods are mostly heuristics, so they are not based on formal models. The hierarchical clustering for image segmentation was implemented by [PERSON] et al. [3] and [PERSON] et al. [4]. Several partitioning algorithm techniques used in image segmentation are K-means cluster [5], K-medoids [6], and fuzzy-based clustering techniques such as Fuzzy C-means (FCM) [7]. [PERSON] [8] stated that traditional methods are in great demand because of their simplicity. However, [PERSON] et al. [9] evaluated the traditional methods and found that those methods were unable to represent real data patterns. Model-based clustering is the most appropriate tool to overcome this problem, especially when facing a limited number of data. One of the methods is known as the Gaussian mixture model (GMM) [10]. In image segmentation, the GMM has several limitations. 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 methods developed to overcome this problem include the use of distributions other than Gaussian, e.g., the Student's \(t\) mixture model (SMM) [12] and the Laplace mixture model (LMM) [13]. A similar idea is also seen in the generalized GMM (GGMM), which utilizes the flexibility of form parameters from the Gaussian distribution [14]. However, the use of a Gaussian distribution still leaves problems regarding the short-tailed form of distribution. The existence of a pattern with a long tail must be approached with more Gaussian distribution components that must compile the Gaussian mixture so that the parsimony of the model is less intact. Some of the mentioned models still apply distributions with rules of symmetry and set the mean as the cluster center; therefore, 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.