Detail publikace

Automated Localization of Temporomandibular Joint Disc in MRI Images

Originální název

Automated Localization of Temporomandibular Joint Disc in MRI Images

Anglický název

Automated Localization of Temporomandibular Joint Disc in MRI Images

Jazyk

en

Originální abstrakt

This paper deals with localization of Temporomandibular Joint Disc (TJD) in Magnetic Resonance Images (MRI). Since the contrast of the TJD is quite low when compared to noise ratio when displayed using MRI, its detection is quite complicated. Therefore the method described in this paper are not not focused the disk itself but detect the most significant objects around TJD, which has usually much higher contrast. For the automatic TJD localization asessment, a training set containing 160 training samples (80 positive and 80 negative) were created and published and several approaches were examined to find the best method. The best results were achieved using support vector machine with Gaussian kernel, which achieved $98.16 \pm 2.81 \%$ accuracy of detection. The creation of the training models for feature extraction and model evaluation was implemented with RapidMiner tool and the IMMI extension. The models created are published at the IMMI extension homepage and they can also serve as a guide to use of the IMMI extension.

Anglický abstrakt

This paper deals with localization of Temporomandibular Joint Disc (TJD) in Magnetic Resonance Images (MRI). Since the contrast of the TJD is quite low when compared to noise ratio when displayed using MRI, its detection is quite complicated. Therefore the method described in this paper are not not focused the disk itself but detect the most significant objects around TJD, which has usually much higher contrast. For the automatic TJD localization asessment, a training set containing 160 training samples (80 positive and 80 negative) were created and published and several approaches were examined to find the best method. The best results were achieved using support vector machine with Gaussian kernel, which achieved $98.16 \pm 2.81 \%$ accuracy of detection. The creation of the training models for feature extraction and model evaluation was implemented with RapidMiner tool and the IMMI extension. The models created are published at the IMMI extension homepage and they can also serve as a guide to use of the IMMI extension.

BibTex


@inproceedings{BUT74813,
  author="Radim {Burget} and Petr {Číka} and Martin {Zukal} and Jan {Mašek}",
  title="Automated Localization of Temporomandibular Joint Disc in MRI Images",
  annote="This paper deals with localization of Temporomandibular Joint Disc (TJD) in Magnetic Resonance Images (MRI). Since the contrast of the TJD is quite low when compared to noise ratio when displayed using MRI, its detection is quite complicated. Therefore the method described in this paper are not not focused the disk itself but detect the most significant objects around TJD, which has usually much higher contrast. For the automatic TJD localization asessment,  a training set containing 160 training samples (80 positive and 80 negative) were created and published and several approaches were examined to find the best method. The best results were achieved using support vector machine with Gaussian kernel, which achieved $98.16 \pm 2.81 \%$ accuracy of detection. The creation of the training models for feature extraction and model evaluation was implemented with RapidMiner tool and the IMMI extension. The models created are published at the IMMI extension homepage and they can also serve as a guide to use of the IMMI extension.",
  booktitle="2011 34th International Conference on Telecommunications and Signal Processing (TSP)",
  chapter="74813",
  howpublished="online",
  year="2011",
  month="august",
  pages="413--416",
  type="conference paper"
}