Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images

The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods fo...

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Vydané v:Journal of medical systems Ročník 41; číslo 1; s. 15
Hlavní autori: Meena Prakash, R., Shantha Selva Kumari, R.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.01.2017
Springer Nature B.V
Predmet:
Accuracy
Algorithms
Bias
Brain - diagnostic imaging
Cerebrospinal fluid
Emerging Technologies for Connected Health
Engineering
Fuzzy Logic
Health Informatics
Health Sciences
Humans
Image & Signal Processing
Image Processing, Computer-Assisted - methods
Labeling
Magnetic Resonance Imaging - methods
Medicine
Medicine & Public Health
Methods
Neuroimaging
Signal processing
Statistics for Life Sciences
India
Fuzzy C means
Expectation maximization
MR brain image segmentation
Gaussian mixture model
ISSN:0148-5598, 1573-689X
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Popis
Shrnutí:The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.
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ISSN:0148-5598
1573-689X
DOI:10.1007/s10916-016-0662-7