Adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation

Overweight and obesity have become a major health concern in the world. Experts believe that fat accumulation in human body (especially at the abdominal zone) has a direct correlation with nonalcoholic fatty liver diseases. Nevertheless, there are no studies that highlight the relationship between...

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Main Author:	Mharrib, Ahmed M.
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Cesarean section Imaging systems
Online Access:	http://psasir.upm.edu.my/id/eprint/47538/1/FK%202012%2082R.pdf
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id	my-upm-ir.47538
record_format	uketd_dc
spelling	my-upm-ir.475382016-07-15T01:14:11Z Adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation 2012-01 Mharrib, Ahmed M. Overweight and obesity have become a major health concern in the world. Experts believe that fat accumulation in human body (especially at the abdominal zone) has a direct correlation with nonalcoholic fatty liver diseases. Nevertheless, there are no studies that highlight the relationship between a realistic representation of the quantity of abdominal fat and the level of diffused fat in the liver. This study aims to investigate the strength and the type of correlation between the indexes of abdominal fat and the level of diffused fat in human liver. Adaptive methods for abdominal fat segmentation and human liver segmentation using CT images are proposed. A modified Fuzzy C mean clustering method and Otsu thresholding technique are employed to segment the CT images of each subject into fat and non-fat tissues individually. Then, the segmented fat tissues in each CT slice are further separated into subcutaneous fat and visceral fat. Finally, the segmented fat tissues in the CT dataset for each subject are used to evaluate the quantities of abdominal fat by dividing the number of fat pixels over the number of total abdominal pixels. The whole liver segmentation procedure is based on processing the CT slices one by one. Gray level, Gaussian gradient, region growing algorithm,distance transformation, canny edge detector and anatomic information are employed together to segment the liver in each CT slice. Then the diffused fat in the segmented liver is evaluated by calculating the mean of liver attenuation (measured in Hounsfield Units) for the segmented liver. The lower the mean value, the lower the tissue density and hence the greater the fat content. Experimental results show that the performances of the abdominal fat segmentation method and the liver segmentation method are very promising. The abdominal fat segmentation method shows a great capability to handle a wide variety of abdominal wall shapes. The liver segmentation method also shows a good performance as well. Several challenges and difficulties due to the similarity of gray level intensities of the liver and the attached organs have been overcome in the proposed liver segmentation method. Data sets of 125 subjects were employed to study the relationship between abdominal fat accumulation and diffused fat in the liver. Experimental results show that there is medium negative correlation between the visceral fat to abdomen size ratio and the mean of liver intensity values (R= - 0.3168, P<0.0005). The same correlation is found between the mean of liver intensity values and the total abdominal fat to abdomen size ratio (R= - 0.3382, P<0.0005). In conclusion, it could be said that the accumulation of abdominal fat is not the main reason for the increase in the level of diffused fat in the liver. However it does somehow contribute towards the process of increasing that level. Cesarean section Imaging systems 2012-01 Thesis http://psasir.upm.edu.my/id/eprint/47538/ http://psasir.upm.edu.my/id/eprint/47538/1/FK%202012%2082R.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Cesarean section Imaging systems
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Cesarean section Imaging systems
spellingShingle	Cesarean section Imaging systems Mharrib, Ahmed M. Adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation
description	Overweight and obesity have become a major health concern in the world. Experts believe that fat accumulation in human body (especially at the abdominal zone) has a direct correlation with nonalcoholic fatty liver diseases. Nevertheless, there are no studies that highlight the relationship between a realistic representation of the quantity of abdominal fat and the level of diffused fat in the liver. This study aims to investigate the strength and the type of correlation between the indexes of abdominal fat and the level of diffused fat in human liver. Adaptive methods for abdominal fat segmentation and human liver segmentation using CT images are proposed. A modified Fuzzy C mean clustering method and Otsu thresholding technique are employed to segment the CT images of each subject into fat and non-fat tissues individually. Then, the segmented fat tissues in each CT slice are further separated into subcutaneous fat and visceral fat. Finally, the segmented fat tissues in the CT dataset for each subject are used to evaluate the quantities of abdominal fat by dividing the number of fat pixels over the number of total abdominal pixels. The whole liver segmentation procedure is based on processing the CT slices one by one. Gray level, Gaussian gradient, region growing algorithm,distance transformation, canny edge detector and anatomic information are employed together to segment the liver in each CT slice. Then the diffused fat in the segmented liver is evaluated by calculating the mean of liver attenuation (measured in Hounsfield Units) for the segmented liver. The lower the mean value, the lower the tissue density and hence the greater the fat content. Experimental results show that the performances of the abdominal fat segmentation method and the liver segmentation method are very promising. The abdominal fat segmentation method shows a great capability to handle a wide variety of abdominal wall shapes. The liver segmentation method also shows a good performance as well. Several challenges and difficulties due to the similarity of gray level intensities of the liver and the attached organs have been overcome in the proposed liver segmentation method. Data sets of 125 subjects were employed to study the relationship between abdominal fat accumulation and diffused fat in the liver. Experimental results show that there is medium negative correlation between the visceral fat to abdomen size ratio and the mean of liver intensity values (R= - 0.3168, P<0.0005). The same correlation is found between the mean of liver intensity values and the total abdominal fat to abdomen size ratio (R= - 0.3382, P<0.0005). In conclusion, it could be said that the accumulation of abdominal fat is not the main reason for the increase in the level of diffused fat in the liver. However it does somehow contribute towards the process of increasing that level.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mharrib, Ahmed M.
author_facet	Mharrib, Ahmed M.
author_sort	Mharrib, Ahmed M.
title	Adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation
title_short	Adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation
title_full	Adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation
title_fullStr	Adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation
title_full_unstemmed	Adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation
title_sort	adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation
granting_institution	Universiti Putra Malaysia
publishDate	2012
url	http://psasir.upm.edu.my/id/eprint/47538/1/FK%202012%2082R.pdf
_version_	1747811934486396928

Adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation

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