Atlas-based segmentation of brain MR images using least square support vector machines
ABSTRACT This study presents an automatic model based technique for brain tissue segmentation from cerebral magnetic resonance (MR) images. In this paper, support vector machine (SVM) based classifier, as a new and powerful kind of supervised machine learning with high generalization characteristics, is employed. Here, least-square SVM (LS-SVM) in conjunction with brain probabilistic atlas as a priori information is applied to obtain class probabilities for three tissues of cerebrospinal fluid (CSF), white matter (WM) and grey matter (GM). The entire process of brain segmentation is performed in an iterative procedure, so that the probabilistic maps of brain tissues will be updated at any iteration. The quantitative and qualitative results indicate excellent performance of the applied method.
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ABSTRACT: One of the primary diagnostic and treatment evaluation tools for brain interpretation has been magnetic resonance imaging (MRI). It has been a widely-used method of high quality medical imaging, especially in brain imaging where MR’s soft tissue contrast and non invasiveness are clear advantages. MR images can also be used to determine a normal and abnormal types of brain. Moreover, the MRI characteristics will help the doctor to avoid the human error in manual interpretation of medical content. Computer-based classification has remained largely experimental work with approaches, one of them is, Support vector machine (SVM). SVM is a pattern recognition algorithm which learns to assign labels to objects through examples. This research paper is an attempt to use SVM to automatically classify brain MRI images under two categories, either normal or abnormal brain which refers to brain tumor. The determination of normal and abnormal brain image is based on symmetry which is exhibited in the axial and coronal images. Using feature vector gained from the MRI images, SVM classifiers are use to classify the images. The process consists of two components which are training phase and a testing phase. Percentage of accuracy on each parameter in SVM will give the idea to choose the best one to be used in further works. Other than that, value of percentage will give the first interpretation either the brain image has the possibility of brain tumor or normal. After all, we are using LabView Advanced Signal Processing Toolkit as the software in our experimental work. We believe with the easiness of this graphical programming and the capabilities of SVM will give a very good result.01/2011;
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ABSTRACT: In healthcare applications, there is tremendous growth in using the computer assistance for effective and fast diagnostic. There are various modalities such as Magnetic resonance imaging (MRI), computed tomography (CT), digital mammography, and others, to provide an insight of subject's body, noninvasively in order to facilitate diagnostic stakeholders to take decision in diagnosis. Being an important step of imaging systems in diagnostic, MRI imaging has been active area for researchers in computational intelligence and image processing. One of the most important problems in image processing and analysis is segmentation and same is true for biomedical imaging. The main objective of segmentation is separating the pixels associated with different types of tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). In this paper, we attempted to optimize the feature set constructed from more than three different types of features. It is well-known fact that, long feature vector representation can be boosting the performance. However, irrelevant feature elements from the long feature vector can become hurdle in convergence of classifier. The optimization feature vector is accomplished using genetic algorithm (GA) with an objective function of maximizing the sum of precision and recall. In addition to the elimination of the feature elements, some elements were also weighted to reduce their effect in the feature matching score. This overall process can also be considered as “fusion of features” for MRI segmentation.Advance Computing Conference (IACC), 2013 IEEE 3rd International; 01/2013