Browsing by Author "Abdulla,Elsafi Ahmed"

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Characterization of Glomerulonephritis and Pyelonephritis using Ultrasonography
(International Journal of Science and Research, 2016) Garelnabi,MEM; Abdulallah,Ibtisam; Abdulla,Elsafi Ahmed; Adam,Mohamed; Hassan,Abdoelrahman
Ultrasound (US) is one modality used to assess renal infections, because it's a simple, Produces image in real time, less expensive, accurate method and well accepted by patient in comparison to other modality. This study aimed to determine the characterization of incidence of renal infections by US, in Khartoum, Wad Madani, Elmanagil hospitals and Elkramit family health center, in urology department. An analytical study on the sonographic pattern of renal infections in 234 person from January 2014 to May 2016. Ultrasound scanning has been carried out, using a curve linear probe with a frequency of 3.5 to 5MHz. this study reveals that female was mostly affected by glomerulonephritis and pyelonephritis rather than male with male to female ratio of 1:1.6 and 1:2 respectively. Flank pain in 82.4% associated with glomerulonephritis while 75% of pyelonephritis showed ill-defined corticomedullary differentiation. In conclusion Ultrasonographic characteristics in addition to medical laboratory test can be used in a multiple linear regression equation to diagnose the patients affect by kidney infection with a classification accuracy of 96%.
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,Mohamed
The 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%.