Detail publikace

Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging

Originální název

Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging

Anglický název

Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging

Jazyk

en

Originální abstrakt

Several methods have been developed for segmentation of MR images. Some of them are fully automated and some of them rely on an expert's assistance, such as determination of a starting point etc. The fully automated methods are usually based on prior knowledge of a given object and can be used only for particular problem. The purpose of the proposed method is a fully automatic segmentation for general MR images independent on the number of tissues present. The proposed method is based on Statistical Region Merging (SRM) algorithm developed by Richard Nock and Frank Nielsen in 2004. The suitable MR contrasts for this algorithm, as it was confirmed during the test phase, are T1, T2 and FLAIR images. The segmentation process divides to image into regions according the properties in the area, but it does not consider the unconnected areas. For this reason, the algorithm is repeated for created segments without a joint border condition. The algorithm was tested on 5000 axial images with resolution 256x256 pixels. In 2256 slices, the tumor was present. Since the proposed method is fully automatic and independent of image intensities, each image of the database can be considered as unique and independent of others. The Dice coefficient for tissue segmentation varies for particular tissues. The best average result was achieved for grey matter, where the dice coefficient reached value 0.84. The results show the suitability of SRM method for multi-contrast MRI segmentation.

Anglický abstrakt

Several methods have been developed for segmentation of MR images. Some of them are fully automated and some of them rely on an expert's assistance, such as determination of a starting point etc. The fully automated methods are usually based on prior knowledge of a given object and can be used only for particular problem. The purpose of the proposed method is a fully automatic segmentation for general MR images independent on the number of tissues present. The proposed method is based on Statistical Region Merging (SRM) algorithm developed by Richard Nock and Frank Nielsen in 2004. The suitable MR contrasts for this algorithm, as it was confirmed during the test phase, are T1, T2 and FLAIR images. The segmentation process divides to image into regions according the properties in the area, but it does not consider the unconnected areas. For this reason, the algorithm is repeated for created segments without a joint border condition. The algorithm was tested on 5000 axial images with resolution 256x256 pixels. In 2256 slices, the tumor was present. Since the proposed method is fully automatic and independent of image intensities, each image of the database can be considered as unique and independent of others. The Dice coefficient for tissue segmentation varies for particular tissues. The best average result was achieved for grey matter, where the dice coefficient reached value 0.84. The results show the suitability of SRM method for multi-contrast MRI segmentation.

BibTex


@inproceedings{BUT109169,
  author="Pavel {Dvořák} and Karel {Bartušek} and Eva {Gescheidtová}",
  title="Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging",
  annote="Several methods have been developed for segmentation of MR images. Some of them are fully automated and some of them rely on an expert's assistance, such as determination of a starting point etc. The fully automated methods are usually based on prior knowledge of a given object and can be used only for particular problem. The purpose of the proposed method is a fully automatic segmentation for general MR images independent on the number of tissues present. The proposed method is based on Statistical Region Merging (SRM) algorithm developed by Richard Nock and Frank Nielsen in 2004. The suitable MR contrasts for this algorithm, as it was confirmed during the test phase, are T1, T2 and FLAIR images. The segmentation process divides to image into regions according the properties in the area, but it does not consider the unconnected areas. For this reason, the algorithm is repeated for created segments without a joint border condition. The algorithm was tested on 5000 axial images with resolution 256x256 pixels. In 2256 slices, the tumor was present. Since the proposed method is fully  automatic and independent of image intensities, each image of the database can be considered as unique and independent of others. The Dice coefficient for tissue segmentation varies for particular tissues.
The best average result was achieved for grey matter, where the dice coefficient reached value 0.84. The results show the suitability of SRM method for multi-contrast MRI segmentation.",
  booktitle="PIERS 2014 Guangzhou Proceedings",
  chapter="109169",
  howpublished="online",
  year="2014",
  month="august",
  pages="1865--1869",
  type="conference paper"
}