Automatic Region-Based Brain Classification of MRI-T1 Data

Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, suc...

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Bibliographic Details
Published in:PloS one 2016-04, Vol.11 (4), p.e0151326-e0151326
Main Authors: Yazdani, Sepideh, Yusof, Rubiyah, Karimian, Alireza, Mitsukira, Yasue, Hematian, Amirshahram
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biology and Life Sciences
Biomedical engineering
Brain
Brain - anatomy & histology
Brain mapping
Brain Mapping - methods
Brain research
Cerebrospinal fluid
Classification
Computational neuroscience
Computer and Information Sciences
Computer simulation
Engineering
Engineering and Technology
Humans
Image classification
Image Interpretation, Computer-Assisted - methods
Image processing
Image segmentation
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical imaging
Medicine and Health Sciences
Methods
Neuroimaging - methods
Normal distribution
Pattern recognition
Physical Sciences
Research and Analysis Methods
Robotics
Substantia alba
Substantia grisea
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Summary:Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared with other segmentation techniques. The obtained results have demonstrated its improved performance and robustness.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0151326