This paper is a critical review of nosologic imaging and its value for childhood brain tumors. This research is done to discuss magnetic resonance spectroscopic imaging (MRSI) as a powerful diagnostic tool and magnetic resonance imaging (MRI) as a tool for assessing brain tumors…
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According to the research findings a latest technique has been established to develop brain nosologic images based on magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance imaging (MRI). Nosologic images give a summary of the distinct lesions and tissues presence in a sole image. This is through pixel or voxel color coding in relation to the assigned histopathological class. The technique proposed utilizes advanced methods that cuts across image processing, recognition of patterns, segments and classification of brain tumors. For better understanding of how it functions, here is an illustration. For purposes of segmentation, a brain atlas that is registered in conjunction with an abnormal tissue that is subject -specific is retrieved from magnetic resonance spectroscopic imaging (MRSI) data. Subsequently, abnormal tissue detected is categorized based on pattern recognition supervised methods. In addition to that, there is computation of class probabilities for the abnormal segmented region. The new technique in comparison to former approaches is extremely flexible. Moreover, it has the capability of exploiting spatial information resulting to nosologic images that are improved. The combination of MRSI and MRI presents a new method of producing nosologic images exhibiting high resolution. Nosologic images with high resolution represent class probabilities and tumor heterogeneity which aid clinicians in making of decisions (Luts et al 2008, p.1). MRSI as a Powerful Diagnostic Tool In the current world, magnetic resonance spectroscopic imaging (MRSI) has been proved to be a diagnostic tool that is non-invasive and remarkably powerful. For instance, its ability of detecting metabolites has been extremely constructive in routine radiologic practices. This is because, it avails essential biochemical information regarding the organism molecule under investigation. In addition to that, magnetic resonance spectroscopy data has been helpful in various techniques such as tissue segmentation. The data has played a critical role in a variety of biomedical applications such as tissue volume quantification, pathologies localization, pre-surgical diagnosis improvement, therapy planning and surgical approach optimization. These applications are significant in solving diverse segmentation problems. For better understanding of various techniques of solving segmentation problems, they have been split into various categories. These are such as, classifiers, thresholding, region growing, models of Markov random field and artificial neural networks. However, Canonical Correlation Analysis (CCA) has been proposed to be a reliable and fast technique for tissue segmentation. CCA is a technique founded on statistical method. Canonical Correlation Analysis has the capability of exploiting simultaneously the spatial and spectral information. The information characterizes the data of Magnetic Resonance Spectroscopic Imaging (MRSI). CCA is successful in the application of functional data of Magnetic Resonance Imaging (MRI). The data has been useful in map sensor, cognitive and motor functions to brain specific areas. Thus, Canonical Correlation Analysis has been adopted for processing of magnetic resonance spectroscopic imaging data for purposes of detecting regions with homogeneous tissue. The regions are such as the sample characterized tumor region. The achievement of ultimate goal is reached via the combination of magnetic resonance spectroscopic spectral-spatial provided information and a subspace signal suitable for spectrum modeling of the tissue type characteristic, whose presence might be in an investigated organ and detection is needed. Canonical Correlation Analysis through the utilization of correlation coefficient quantifies the correlation between dual variable sets, and the spectra magnitude of the data measured and subspace signal. Afterwards, there is exploitation of the coefficients for
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