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Automated MRI segmentation techniques are helpful for a physician for early diagnosis of degenerating diseases in individual patients. Here we are using the T1weighted axial MR images of neuro degenerative diseases. The assessment of the accuracy of the result is done by an expert. FCM an unsupervised clustering technique is implemented in order to classify the brain voxel. The brain voxels are classified...
At its simplest, volume calculation of MR Image segmented & further soft computed to estimate the affected intensity of Alzheimer's disease is dealt with this paper. It is concerned with Voxel Based Morphometry to render the first part segmentation. The result gives an active region which further needs an estimation to justify the diagnosis. As in this case the image is in form of voxels. When...
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...
We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed...
The electrical conductivity of human tissue could be used as an additional diagnostic parameter or might be helpful for the prediction of the local SAR during MR measurements. In this study, the approach ldquoElectric Properties Tomographyrdquo (EPT) is applied, which derives the patient's electric conductivity using a standard MR system. To this goal, the spatial transmit sensitivity distribution...
When using low power laser in therapy and diagnostics, the knowledge about the relationship between laser wavelengths and the percentage of reflected energy in the interface air-tissue and the distribution of energy in tissue helps us to choose appropriate wavelengths. In this paper, we present some results obtained from the simulation of low power 633, 780, 850, and 940 nm laser in brain by Monte...
The detection of Multiple Sclerosis (MS) lesions in Magnetic Resonance (MR) images remains an important issue in medical image processing. Diagnostic criteria for MS based on brain MRI concern mainly dissemination in space and time. In this context, this paper describes a novel region- based approach to automatically count the number of MS lesions present in a set of MR images. Given a set of candidate...
An automatic 3D segmentation based on fuzzy connectedness (FQ) is proposed for MRI brain images. The main contribution of the present paper includes two parts: the accurate extraction of brain tissues and the automatic selection of the seed of FC. The brain tissues are extracted through obtaining approximate region of brain tissues via improved region growing, choosing the optimal threshold value...
A laser Doppler system for intracerebral measurements during stereotactic and functional neurosurgery is presented. The system comprises a laser Doppler perfusion monitor, an optical probe adapted for the Leksellreg stereotactic system and a personal computer with software for acquisition, data analysis and presentation. The software makes it possible to present both the perfusion and the total backscattered...
A detailed analysis procedure is described for segmenting T1-weighted volumetric MR brain into different tissue types based on fuzzy classification. The main aim in this study is to compensate for the blurring effect on tissue boundaries due to partial volume effects. The method used in this paper is described as follows. First, the intracranial volume (ICV) is separated from the scalp and skull with...
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