TY - GEN
T1 - Mixture model for image segmentation using Gaussian, Student's t, and Laplacian distribution with spatial dependence
AU - Iriawan, Nur
AU - Pravitasari, Anindya Apriliyanti
AU - Irhamah,
AU - Fithriasari, Kartika
AU - Purnami, Santi Wulan
AU - Ferriastuti, Widiana
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/12/18
Y1 - 2019/12/18
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85077713101&partnerID=8YFLogxK
U2 - 10.1063/1.5139774
DO - 10.1063/1.5139774
M3 - Conference contribution
AN - SCOPUS:85077713101
T3 - AIP Conference Proceedings
BT - 2nd International Conference on Science, Mathematics, Environment, and Education
A2 - Indriyanti, Nurma Yunita
A2 - Ramli, Murni
A2 - Nurhasanah, Farida
PB - American Institute of Physics Inc.
T2 - 2nd International Conference on Science, Mathematics, Environment, and Education, ICoSMEE 2019
Y2 - 26 July 2019 through 28 July 2019
ER -