Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU110262" target="_blank" >RIV/00216305:26220/14:PU110262 - isvavai.cz</a>

Alternative codes found

RIV/68081731:_____/14:00432483

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2014

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

PIERS 2014 Guangzhou Proceedings

ISBN

978-1-934142-28-8

ISSN

1559-9450

e-ISSN

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Number of pages

5

Pages from-to

1865-1869

Publisher name

Neuveden

Place of publication

Guangzhou

Event location

Guangzhou

Event date

Aug 25, 2014

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000393225900413