Research Papers
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Item Characterization of Non-Small Cell Lung Carcinoma Gross Target Volume with 18F-FDG PET scan using Texture Analysis(International Journal Dental and Medical Sciences Research (IJDMSR), 2020) Awadain,Sami Y. I; Alameen,Suhaib; Algorashi,Eman M; Gar-Elnabi,Mohamed E. MThis study concern to characterize the lung area to cardiac, lung, tumor and submucosal using Gray Level Co-occurrence Matrix (GLCM) and extract classification features from PET/CT with fluorine-18 fluorodeoxyglucose images. Using the GLCM techniques to find the gray level variation in PET/CT images it complements the features extracted from PET/CT images with variation of gray level in pixels and estimate the distribution of the sub-patterns using Interactive Data Language IDL software. The results show’s that the Gray Level Co-occurrence Matrix and features extracted give a classification accuracy of cardiac 91.6%, lung 100%, tumor 99.6%, while the sub-mucosal showed accuracy 91.2%. The overall classification accuracy of lung area 96.0%. These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new PET/CT images with the appropriate lung area names.Item Characterization of Kidney Infection in Ultrasound B-mode Images Using Texture Analysis(International Journal of Science and Research, 2016) Garelnabi,MEM; Abdulallah,Ibtisam; Bakry,A. H. A; Abdulla,Elsafi Ahmed; Adam,MohamedThe general objective of this study was to develop an algorithm that can extracted textural features from ultrasound images of normal and abnormal kidneys in order to classify these images as having normal tissues, glomerulonephritis, or pyelonephritis. Linear discriminant analysis was used to classify the extracted features from the medulla and pelvic calycle system of kidneys ultrasound images. The results of the study showed that the overall accuracy using medulla texture equal to 98% while for those extracted from pelvic calycle system was 95.7. In conclusion linear function was developed to classify other ultrasound images with an error <5%.