Detail publikace

X-ray Image Processing in Studying Jawbone Tissues

Originální název

X-ray Image Processing in Studying Jawbone Tissues

Anglický název

X-ray Image Processing in Studying Jawbone Tissues

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

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"
}