Abstract

MRI grayscale image data pattern, in reality, is not always symmetrical. Sometimes, it has a skewed pattern, leptokurtic, mesokurtic, platykurtic and even fat-tail in distribution. The Gaussian approach is not always able to explain this kind of data pattern. Therefore, other approaches to employ other distributions are used to overcome this problem. This study tries to compare the Gaussian, Student's t, and Laplacian mixture model in the modeling of MRI brain tumor image segmentation. In addition, we used the Markov Random Field as the prior to the spatial dependence. The cluster validation is done by calculating the Silhouette Index (SI) and Misclassification Ratio (MCR). The results demonstrate that for the skewed and leptokurtic data pattern, the Laplacian mixture model shows the best representation, while the Student's t mixture model has a great performance in fat-tail data pattern and also more robust against noise.

Original languageEnglish
Title of host publication2nd International Conference on Science, Mathematics, Environment, and Education
EditorsNurma Yunita Indriyanti, Murni Ramli, Farida Nurhasanah
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735419452
DOIs
Publication statusPublished - 18 Dec 2019
Event2nd International Conference on Science, Mathematics, Environment, and Education, ICoSMEE 2019 - Surakarta, Indonesia
Duration: 26 Jul 201928 Jul 2019

Publication series

NameAIP Conference Proceedings
Volume2194
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2nd International Conference on Science, Mathematics, Environment, and Education, ICoSMEE 2019
Country/TerritoryIndonesia
CitySurakarta
Period26/07/1928/07/19

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