A software tool for automatic classification and segmentation of 2D/3D medical images

Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and...

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Vydané v:Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Ročník 702; s. 137 - 140
Hlavní autori: Strzelecki, Michal, Szczypinski, Piotr, Materka, Andrzej, Klepaczko, Artur
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 21.02.2013
Predmet:
Classification
Data classification
Image analysis and processing software
Magnetic resonance imaging
Medical imaging
Positron emission
Segmentation
Surface layer
Texture
Texture analysis
Three dimensional
Tomography
Data classification
Texture analysis
Image analysis and processing software
Magnetic resonance imaging
ISSN:0168-9002, 1872-9576
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Popis
Shrnutí:Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0168-9002
1872-9576
DOI:10.1016/j.nima.2012.09.006