DocumentCode :
2795667
Title :
Local maximum detection for fully automatic classification of EM algorithm
Author :
Lerddararadsamee, Thararin ; Jiraraksopakun, Yuttapong
Author_Institution :
Electron. & Telecommun. Eng. Dept., King Mongkut´´s Univ. of Technol. Thonburi, Bangkok, Thailand
fYear :
2012
fDate :
16-18 May 2012
Firstpage :
1
Lastpage :
4
Abstract :
In this paper, we proposed a method for fully-automatic EM segmentation on brain MR images without a priori knowledge. Instead of manually predetermination on number of tissue classes, the proposed method automatically find mean intensities of distinct tissues from the histogram. The brain MR images were chosen to test our proposed method, but our method can, in fact, be general for other MR segmentations using EM with which the Gaussian mixture distribution of an image histogram holds. The results from our method suggested that a fully automatic segmentation using EM can be achieved with no significant difference in segmentation accuracy compared to the conventional EM.
Keywords :
Gaussian distribution; biological tissues; biomedical MRI; brain; expectation-maximisation algorithm; image classification; image segmentation; medical image processing; Gaussian mixture distribution; automatic expectation maximization segmentation; brain MRI segmentation; expectation maximization algorithm; fully automatic classification; image histogram; local maximum detection; tissue classes; Accuracy; Brain models; Classification algorithms; Histograms; Image segmentation; Automatic segmentation; Expectation Maximization (EM); Magnetic Resonance Image (MRI); local maximum detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2012 9th International Conference on
Conference_Location :
Phetchaburi
Print_ISBN :
978-1-4673-2026-9
Type :
conf
DOI :
10.1109/ECTICon.2012.6254193
Filename :
6254193
Link To Document :
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