Publication detail
X-ray Image Processing in Studying Jawbone Tissues
MIKULKA, J. KABRDA, M.
Original Title
X-ray Image Processing in Studying Jawbone Tissues
English Title
X-ray Image Processing in Studying Jawbone Tissues
Type
conference paper
Language
en
Original Abstract
Image processing in biomedical applications is strongly developing issue. Many methods and approaches for image preprocessing, segmentation and visualization were described. This paper describes X-ray image processing. The aim of processing is to segment regions of jawbone cysts and evaluate their local descriptors. It is necessary to choose suitable segmentation method because of adverse parameters of regions. The regions of the cysts are of low contrast and the pixel intensity distribution is not homogenous. The semiautomatic live-wire method was chosen. This approach to image segmentation is faster and more accurate than manual segmentation. It is very good compromise between simple manual edge tracing and automatic methods such as thresholding, watershed segmentation or other methods whose results must be post-processed. The second step of processing is to evaluate local descriptors of segmented regions which correspond to cysts. Several parameters were chosen to describe these regions: region area, mean gray value of intensities, modal gray value of intensities, standard deviation of intensities, minimal and maximal gray value of intensities, integrated intensity, median of intensities and shape descriptors of region (perimeter, circularity, aspect ratio, roundness and solidity). Values of these parameters will be used in following development of semiautomatic processing method with regard to current assessment of cysts by doctors.
English abstract
Image processing in biomedical applications is strongly developing issue. Many methods and approaches for image preprocessing, segmentation and visualization were described. This paper describes X-ray image processing. The aim of processing is to segment regions of jawbone cysts and evaluate their local descriptors. It is necessary to choose suitable segmentation method because of adverse parameters of regions. The regions of the cysts are of low contrast and the pixel intensity distribution is not homogenous. The semiautomatic live-wire method was chosen. This approach to image segmentation is faster and more accurate than manual segmentation. It is very good compromise between simple manual edge tracing and automatic methods such as thresholding, watershed segmentation or other methods whose results must be post-processed. The second step of processing is to evaluate local descriptors of segmented regions which correspond to cysts. Several parameters were chosen to describe these regions: region area, mean gray value of intensities, modal gray value of intensities, standard deviation of intensities, minimal and maximal gray value of intensities, integrated intensity, median of intensities and shape descriptors of region (perimeter, circularity, aspect ratio, roundness and solidity). Values of these parameters will be used in following development of semiautomatic processing method with regard to current assessment of cysts by doctors.
Keywords
X-ray, jawbone, cysts, necrosis, image segmentation, image classification
RIV year
2012
Released
19.08.2012
Location
Moscow
ISBN
978-1-934142-22-6
Book
PIERS 2012 in Moscow Proceedings
Pages from
145
Pages to
148
Pages count
4
BibTex
@inproceedings{BUT93421,
author="Jan {Mikulka} and Miroslav {Kabrda}",
title="X-ray Image Processing in Studying Jawbone Tissues",
annote="Image processing in biomedical applications is strongly developing issue. Many methods and approaches for image preprocessing, segmentation and visualization were described. This paper describes X-ray image processing. The aim of processing is to segment regions of jawbone cysts and evaluate their local descriptors. It is necessary to choose suitable segmentation method because of adverse parameters of regions. The regions of the cysts are of low contrast and the pixel intensity distribution is not homogenous. The semiautomatic live-wire method was chosen. This approach to image segmentation is faster and more accurate than manual segmentation. It is very good compromise between simple manual edge tracing and automatic methods such as thresholding, watershed segmentation or other methods whose results must be post-processed. The second step of processing is to evaluate local descriptors of segmented regions which correspond to cysts. Several parameters were chosen to describe these regions: region area, mean gray value of intensities, modal gray value of intensities, standard deviation of intensities, minimal and maximal gray value of intensities, integrated intensity, median of intensities and shape descriptors of region (perimeter, circularity, aspect ratio, roundness and solidity). Values of these parameters will be used in following development of semiautomatic processing method with regard to current assessment of cysts by doctors.",
booktitle="PIERS 2012 in Moscow Proceedings",
chapter="93421",
howpublished="electronic, physical medium",
year="2012",
month="august",
pages="145--148",
type="conference paper"
}