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
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