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Automated glioma detection and segmentation using graphical models
- Source :
- PLoS ONE, PLoS ONE, Vol 13, Iss 8, p e0200745 (2018)
- Publication Year :
- 2018
- Publisher :
- Public Library of Science, 2018.
-
Abstract
- Glioma detection and segmentation is a challenging task for radiologists and clinicians. The research reported in this paper seeks to develop a better clinical decision support algorithm for clinicians diagnosis. This paper presents a probabilistic method for detection and segmentation between abnormal tissue regions and brain tumour (tumour core and edema) portions from Magnetic Resonance Imaging (MRI). A framework is constructed to learn structure of undirected graphical models that can represent the spatial relationships among variables and apply it to glioma segmentation. Compared with the pixel of image, the superpixel is more consistent with human visual cognition and contains less redundancy, thus, the superpixels are considered as the basic unit of structure learning and glioma segmentation scheme. l1-regularization techniques are applied to learn the appropriate structure for modeling graphical models. Conditional Random Fields (CRF) are used to model the spatial interactions among image superpixel regions and their measurements. A number of features including statistics features, the combined features from the local binary pattern as well as gray level run length, curve features, and fractal features were extracted from each superpixel. The features are then passed by l1-regularization to ensure a robust classification. The proposed method is compared with support vector machine and Fuzzy c-means to classify each superpixel into normal and abnormal tissue. The proposed system is tested for the presence of low grade as well as high grade glioma tumors on images collected from BRATS2013, BRATS2015 data set and Henan Provincial People's Hospital (HNPPH) data set. The experiments performed provides similarity between segmented and truth image up to 91.5% by correlation method.
- Subjects :
- Conditional random field
Computer science
lcsh:Medicine
Social Sciences
02 engineering and technology
030218 nuclear medicine & medical imaging
Diagnostic Radiology
Machine Learning
0302 clinical medicine
Learning and Memory
0202 electrical engineering, electronic engineering, information engineering
Medicine and Health Sciences
Image Processing, Computer-Assisted
Psychology
Segmentation
Graphical model
lcsh:Science
Neurological Tumors
Multidisciplinary
Brain Neoplasms
Radiology and Imaging
Glioma
Magnetic Resonance Imaging
Fractals
Oncology
Neurology
Physical Sciences
020201 artificial intelligence & image processing
Algorithms
Research Article
Optimization
Computer and Information Sciences
Local binary patterns
Imaging Techniques
Geometry
Image processing
Research and Analysis Methods
03 medical and health sciences
Fractal
Diagnostic Medicine
Artificial Intelligence
Support Vector Machines
Image Interpretation, Computer-Assisted
Learning
Humans
Pixel
business.industry
lcsh:R
Cognitive Psychology
Cancers and Neoplasms
Biology and Life Sciences
Reproducibility of Results
Pattern recognition
Random Variables
Probability Theory
Support vector machine
Data set
ROC Curve
Cognitive Science
lcsh:Q
Artificial intelligence
Neoplasm Grading
business
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....2f56b5e636eb42b08f5d98098d585e05