Detail publikace

Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools

Originální název

Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools

Anglický název

Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools

Jazyk

en

Originální abstrakt

Using magnetic resonance tomography to scan biological tissues is currently a very dynamic approach. Based on various image parameters, the method enables us to analyze tissue properties, recognize healthy and pathological tissues, and diagnose the disease or indicate its progression. These activities are then necessarily accompanied by the processing of the acquired images. The paper introduces a comparison of statistical tools for the trainable segmentation of multiparametric data obtained through magnetic resonance tomography. In this context, the author briefly compares various available tools (Weka, Slicer3D, and RapidMiner) in view of the input data training and testing, applicability of the classification models, and ability of the input/output data to be extended with other systems for further processing. The paper also describes as a multiparametric task the segmentation of a brain tumor performed with real MR data. The source of the data consists in T1 and T2-weighted images. The proposed segmentation method is carried out within the following phases: data resampling; spatial data coregistration; definition of the training points; training of the SVM classification model; testing of the model and interpretation of the classification results.

Anglický abstrakt

Using magnetic resonance tomography to scan biological tissues is currently a very dynamic approach. Based on various image parameters, the method enables us to analyze tissue properties, recognize healthy and pathological tissues, and diagnose the disease or indicate its progression. These activities are then necessarily accompanied by the processing of the acquired images. The paper introduces a comparison of statistical tools for the trainable segmentation of multiparametric data obtained through magnetic resonance tomography. In this context, the author briefly compares various available tools (Weka, Slicer3D, and RapidMiner) in view of the input data training and testing, applicability of the classification models, and ability of the input/output data to be extended with other systems for further processing. The paper also describes as a multiparametric task the segmentation of a brain tumor performed with real MR data. The source of the data consists in T1 and T2-weighted images. The proposed segmentation method is carried out within the following phases: data resampling; spatial data coregistration; definition of the training points; training of the SVM classification model; testing of the model and interpretation of the classification results.

BibTex


@inproceedings{BUT109610,
  author="Jan {Mikulka}",
  title="Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools",
  annote="Using magnetic resonance tomography to scan biological tissues is currently a very dynamic approach. Based on various image parameters, the method enables us to analyze tissue properties, recognize healthy and pathological tissues, and diagnose the disease or indicate its progression. These activities are then necessarily accompanied by the processing of the acquired images. The paper introduces a comparison of statistical tools for the trainable segmentation of multiparametric data obtained through magnetic resonance tomography. In this context, the author briefly compares various available tools (Weka, Slicer3D, and RapidMiner) in view of the input data training and testing, applicability of the classification models, and ability of the input/output data to be extended with other systems for further processing. The paper also describes as a multiparametric task the segmentation of a brain tumor performed with real MR data. The source of the data consists in T1 and T2-weighted images. The proposed segmentation method is carried out within the following phases: data resampling; spatial data coregistration; definition of the training points; training of the SVM classification model; testing of the model and interpretation of the classification results.",
  booktitle="Proceedings of PIERS 2014 in Guangzhou",
  chapter="109610",
  howpublished="online",
  year="2014",
  month="september",
  pages="1861--1864",
  type="conference paper"
}