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@article{1193195,
author = {Mikulka, Jan and Gescheidtová, Eva and Kabrda, Miroslav and Peřina, Vojtěch},
article_location = {Praha},
article_number = {1},
keywords = {Image processing; image classification; follicular cyst; radicular cyst; live-wire; level set; OPG; RTG},
language = {eng},
issn = {1210-2512},
journal = {Radioengineering},
title = {Classification of Jaw Bone Cysts and Necrosis via the Processing of Orthopantomograms},
url = {http://www.radioeng.cz/fulltexts/2013/13_01_0114_0122.pdf},
volume = {22},
year = {2013}
}
TY - JOUR
ID - 1193195
AU - Mikulka, Jan - Gescheidtová, Eva - Kabrda, Miroslav - Peřina, Vojtěch
PY - 2013
TI - Classification of Jaw Bone Cysts and Necrosis via the Processing of Orthopantomograms
JF - Radioengineering
VL - 22
IS - 1
SP - 114-122
EP - 114-122
PB - SPOLECNOST PRO RADIOELEKTRONICKE INZENYRSTVI, CZECH TECHNICAL UNIVERSITY, DEPT OF ELECTROMAGNETIC FIELD
SN - 12102512
KW - Image processing
KW - image classification
KW - follicular cyst
KW - radicular cyst
KW - live-wire
KW - level set
KW - OPG
KW - RTG
UR - http://www.radioeng.cz/fulltexts/2013/13_01_0114_0122.pdf
N2 - The authors analyze the design of a method for automatized evaluation of parameters in orthopantomographic images capturing pathological tissues developed in human jaw bones. The main problem affecting the applied medical diagnostic procedures consists in low repeatability of the performed evaluation. This condition is caused by two aspects, namely subjective approach of the involved medical specialists and the related exclusion of image processing instruments from the evaluation scheme. The paper contains a description of the utilized database containing images of cystic jaw bones; this description is further complemented with appropriate schematic representation. Moreover, the authors present the results of fast automatized segmentation realized via the live-wire method and compare the obtained data with the results provided by other segmentation techniques. The shape parameters and the basic statistical quantities related to the distribution of intensities in the segmented areas are selected. The evaluation results are provided in the final section of the study; the authors correlate these values with the subjective assessment carried out by radiologists. Interestingly, the paper also comprises a discussion presenting the possibility of using selected parameters or their combinations to execute automatic classification of cysts and osteonecrosis. In this context, a comparison of various classifiers is performed, including the Decision Tree, Naive Bayes, Neural Network, k-NN, SVM, and LDA classification tools. Within this comparison, the highest degree of accuracy (85% on the average) can be attributed to the Decision Tree, Naive Bayes, and Neural Network classifiers.
ER -
MIKULKA, Jan, Eva GESCHEIDTOVÁ, Miroslav KABRDA a Vojtěch PEŘINA. Classification of Jaw Bone Cysts and Necrosis via the Processing of Orthopantomograms. \textit{Radioengineering}. Praha: SPOLECNOST PRO RADIOELEKTRONICKE INZENYRSTVI, CZECH TECHNICAL UNIVERSITY, DEPT OF ELECTROMAGNETIC FIELD, 2013, roč.~22, č.~1, s.~114-122. ISSN~1210-2512.
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