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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| Veröffentlicht in: | Journal of medical systems 2017, Vol.41 (1), p.15-15, Article 15 |
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| creator | Meena Prakash, R. Shantha Selva Kumari, R. |
| description | 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%. |
| doi_str_mv | 10.1007/s10916-016-0662-7 |
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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%.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Bias</subject><subject>Brain - diagnostic imaging</subject><subject>Cerebrospinal fluid</subject><subject>Emerging Technologies for Connected Health</subject><subject>Engineering</subject><subject>Fuzzy Logic</subject><subject>Health Informatics</subject><subject>Health Sciences</subject><subject>Humans</subject><subject>Image & Signal Processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Labeling</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Methods</subject><subject>Neuroimaging</subject><subject>Signal processing</subject><subject>Statistics for Life 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Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images</title><author>Meena Prakash, R. ; Shantha Selva Kumari, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-10a8d550012112984b52a7f103073da75301a759746a7e4e3b38b1589bb04ac73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Bias</topic><topic>Brain - diagnostic imaging</topic><topic>Cerebrospinal fluid</topic><topic>Emerging Technologies for Connected Health</topic><topic>Engineering</topic><topic>Fuzzy Logic</topic><topic>Health Informatics</topic><topic>Health Sciences</topic><topic>Humans</topic><topic>Image & Signal Processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Labeling</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Medicine</topic><topic>Medicine & 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| subjects | 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 |
| title | Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images |
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