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MRI Segmentation : Methods and Comparison

Prof. Vikas Gupta; Ms. Rashmi S. Gadekar
We present a MR image segmentation algorithm based on the conventional Expectation Maximization (EM) algorithm and the multiresolution analysis of images. Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. Magnetic resonance (MR) imaging offers more accurate information for medical examination than other medical images such as X-ray, ultrasonic and CT images. The goal of magnetic resonance (MR) image segmentation is to accurately identify the principal tissue structures in these image volumes. The experimental results show that the proposed segmentation algorithm is appropriate for classifying a large amount of axial brain MR data, and also show that the proposed lesion detection algorithm is successful. The algorithm provides good-quality segmented brain images a very quick way, which makes it an excellent tool to support virtual brain endoscopy. Two data sets are used to test the performance of the EM and the proposed Gaussian Multiresolution EM, GMEM, algorithm. The results, which proved more accurate segmentation by the GMEM algorithm compared to that of the EM algorithm, are represented statistically and graphically to give deep understanding.
Select Volume / Issues:
Year:
2012
Type of Publication:
Article
Keywords:
MR image segmentation; EM algorithm; Multiresolution analysis; Gaussian Mixture model; Maximum likelihood estimator
Journal:
IJECCE
Volume:
3
Number:
5
Pages:
1162-1167
Month:
Sept.
Hits: 1750

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