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Abstract: | The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing |
Description: | International Journal of Emerging Technologies in Computational and Applied Sciences, 7(1), December 2013- February, 2014, pp. 08-14 |
URI: | http://dyuthi.cusat.ac.in/purl/4579 |
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Automatic Detec ... g Statistical Features.pdf | (413.4Kb) |
Abstract: | Yeasts are ubiquitous in their distribution and populations mainly depend on the type and concentration of organic materials. The distribution of species, as well as their numbers and metabolic characteristics were found to be governed by existing environmental conditions. Marine yeasts were first discovered from the Atlantic Ocean and following this discovery, yeasts were isolated from different sources, viz. seawater, marine deposits, seaweeds, fish, marine mammals and sea birds. Nearshore environments are usually inhabited by tens to thousands of cells per litre of water, whereas low organic surface to deep-sea oceanic regions contain 10 or fewer cells/litre. Aerobic forms are found more in clean waters and fermentative forms in polluted waters. Yeasts are more abundant in silty muds than in sandy sediments. The isolation frequency of yeasts fell as the depth of the sampling site is increased. Major genera isolated in this study were Candida, Cryptococcus, Debaryomyces and Rhodotorula. For biomass estimation ergosterol method was used. Classification and identification of yeasts were performed using different criteria, i.e. morphology, sexual reproduction and physiological/biochemical characteristics. Fatty acid profiling or molecular sequencing of the IGS and ITS regions and 28S gene rDNA ensured accurate identification. |
URI: | http://dyuthi.cusat.ac.in/xmlui/purl/2035 |
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Marine yeasts-a rewiew.pdf | (214.2Kb) |
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