Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff

The conventional criterion for fi"acture risk assessment is measured based on bone mineral density (BMD) value that is produce by X-ray. Even if there is a strong association between bone strength and BMD, nowadays it is well accepted that this is not sufficiently reliable predictor of fi^ct...

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Main Author:	Md Ariff, Norharyati
Format:	Thesis
Language:	English
Published:	2007
Subjects:	Electronic Computers Computer Science
Online Access:	https://ir.uitm.edu.my/id/eprint/1818/1/TD_NORHARYATI%20MD%20ARIFF%20CS%2007_5%20P01.pdf
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id	my-uitm-ir.1818
record_format	uketd_dc
spelling	my-uitm-ir.18182019-11-15T02:33:21Z Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff 2007 Md Ariff, Norharyati Electronic Computers. Computer Science The conventional criterion for fi"acture risk assessment is measured based on bone mineral density (BMD) value that is produce by X-ray. Even if there is a strong association between bone strength and BMD, nowadays it is well accepted that this is not sufficiently reliable predictor of fi^cture risk in osteoporotic patients. Therefore, there is a growing need for better predictor of bone strength. The image processing is one of the methods to measure the bone strength. It is need to get the optimal result of osteoperosis or osteopenia detection. Using image segmentation, the bone strength can be measure by looking for the great image on the length of the outline cortical. For the image segmentation, genetic algorithm are use to segment the bone image and it is a new method that applies in the image processing field. Genetic algorithms have several steps. For the initial population, 200 pixels will be taken to be the initial population in randomly. From the initial population, the fitaess fimction has to calculate to get the fittest pixels. Fitaess fimction is calculated based on the gradient which is the length fi*om each pixel. The range of pixel value and the position between the characteristic of the bone are define. The fittest pixel values that are fit with the characteristic are selected to make the crossover and mutation. After that, the next generation will be process until it get the satisfy value to get the optimum line between the cortical bone and trabecular bone. The objectives has been achieved and found the outline of the cortical bone. The results come out with the mean and standard deviation of the cortical bone length. The prototype is capable to shows the outline of the cortical bone and the accuracy of the outline is 30% to 50% after make the comparison fi-om other researcher. Faculty of Computer and Mathematical Sciences 2007 Thesis https://ir.uitm.edu.my/id/eprint/1818/ https://ir.uitm.edu.my/id/eprint/1818/1/TD_NORHARYATI%20MD%20ARIFF%20CS%2007_5%20P01.pdf text en public degree Universiti Teknologi MARA Faculty of Computer and Mathematical Sciences
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Electronic Computers Computer Science
spellingShingle	Electronic Computers Computer Science Md Ariff, Norharyati Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff
description	The conventional criterion for fi"acture risk assessment is measured based on bone mineral density (BMD) value that is produce by X-ray. Even if there is a strong association between bone strength and BMD, nowadays it is well accepted that this is not sufficiently reliable predictor of fi^cture risk in osteoporotic patients. Therefore, there is a growing need for better predictor of bone strength. The image processing is one of the methods to measure the bone strength. It is need to get the optimal result of osteoperosis or osteopenia detection. Using image segmentation, the bone strength can be measure by looking for the great image on the length of the outline cortical. For the image segmentation, genetic algorithm are use to segment the bone image and it is a new method that applies in the image processing field. Genetic algorithms have several steps. For the initial population, 200 pixels will be taken to be the initial population in randomly. From the initial population, the fitaess fimction has to calculate to get the fittest pixels. Fitaess fimction is calculated based on the gradient which is the length fi*om each pixel. The range of pixel value and the position between the characteristic of the bone are define. The fittest pixel values that are fit with the characteristic are selected to make the crossover and mutation. After that, the next generation will be process until it get the satisfy value to get the optimum line between the cortical bone and trabecular bone. The objectives has been achieved and found the outline of the cortical bone. The results come out with the mean and standard deviation of the cortical bone length. The prototype is capable to shows the outline of the cortical bone and the accuracy of the outline is 30% to 50% after make the comparison fi-om other researcher.
format	Thesis
qualification_level	Bachelor degree
author	Md Ariff, Norharyati
author_facet	Md Ariff, Norharyati
author_sort	Md Ariff, Norharyati
title	Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff
title_short	Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff
title_full	Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff
title_fullStr	Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff
title_full_unstemmed	Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff
title_sort	metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / norharyati md ariff
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2007
url	https://ir.uitm.edu.my/id/eprint/1818/1/TD_NORHARYATI%20MD%20ARIFF%20CS%2007_5%20P01.pdf
_version_	1783733059693051904

Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff

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