Publication detail

The Statistical Evaluation of Data Obtained via the Manual Segmentation of MRI images of a Pathological Tissue

MARCOŇ, P. BARTUŠEK, K. ŠPRLÁKOVÁ, A. MIKULKA, J. GESCHEIDTOVÁ, E.

Original Title

The Statistical Evaluation of Data Obtained via the Manual Segmentation of MRI images of a Pathological Tissue

English Title

The Statistical Evaluation of Data Obtained via the Manual Segmentation of MRI images of a Pathological Tissue

Type

conference paper

Language

en

Original Abstract

The authors present a statistical evaluation of the MRI images of bones and soft tissues. MRI imaging of small bones is a problem issue because the measured bone images are loaded with susceptibility artifacts and low signal-to-noise ratio. Therefore, we compare the commonly used MRI contrast and looking for the best contrast to bone marrow imaging. The acquired images are classified in terms of the signal-to-noise ratio, intensity difference and steepness of the edges. The aim of this research was to statistically evaluate and determine the deviation of MRI images; this step was performed by several experts. These specialists conducted proper segmenting of pathological and healthy tissues, and they also compared the selected areas. In some cases, especially when the boundaries of the area are not clearly visible, the problem is to delimit the bone marrow area. Therefore, it is not possible to use automatic segmentation algorithms, and we focused on the manual evaluation of the statistical data in the area of the patient's bone marrow pathology (e.g. the probability of cyst formation can be determined from abnormal levels of vitamin D and osteocalcin).

English abstract

The authors present a statistical evaluation of the MRI images of bones and soft tissues. MRI imaging of small bones is a problem issue because the measured bone images are loaded with susceptibility artifacts and low signal-to-noise ratio. Therefore, we compare the commonly used MRI contrast and looking for the best contrast to bone marrow imaging. The acquired images are classified in terms of the signal-to-noise ratio, intensity difference and steepness of the edges. The aim of this research was to statistically evaluate and determine the deviation of MRI images; this step was performed by several experts. These specialists conducted proper segmenting of pathological and healthy tissues, and they also compared the selected areas. In some cases, especially when the boundaries of the area are not clearly visible, the problem is to delimit the bone marrow area. Therefore, it is not possible to use automatic segmentation algorithms, and we focused on the manual evaluation of the statistical data in the area of the patient's bone marrow pathology (e.g. the probability of cyst formation can be determined from abnormal levels of vitamin D and osteocalcin).

Keywords

MRI images, pathological tissues, manual segmentation, statistical analysis

RIV year

2014

Released

25.08.2014

Location

Guangzhou, China

ISBN

978-1-934142-28-8

Book

PIERS 2014 Guangzhou Proceedings

Pages from

1898

Pages to

1901

Pages count

4

URL

BibTex


@inproceedings{BUT109594,
  author="Petr {Marcoň} and Karel {Bartušek} and Andrea {Šprláková} and Jan {Mikulka} and Eva {Gescheidtová}",
  title="The Statistical Evaluation of Data Obtained via the Manual Segmentation of MRI images of a Pathological Tissue",
  annote="The authors present a statistical evaluation of the MRI images of bones and soft tissues. MRI imaging of small bones is a problem issue because the measured bone images are loaded with susceptibility artifacts and low signal-to-noise ratio. Therefore, we compare the commonly used MRI contrast and looking for the best contrast to bone marrow imaging. The acquired images are classified in terms of the signal-to-noise ratio, intensity difference and steepness of the edges.
The aim of this research was to statistically evaluate and determine the deviation of MRI images; this step was performed by several experts. These specialists conducted proper segmenting of pathological and healthy tissues, and they also compared the selected areas. In some cases, especially when the boundaries of the area are not clearly visible, the problem is to delimit the bone marrow area. Therefore, it is not possible to use automatic segmentation algorithms, and we focused on the manual evaluation of the statistical data in the area of the patient's bone marrow pathology (e.g. the probability of cyst formation can be determined from abnormal levels of vitamin D and osteocalcin).",
  booktitle="PIERS 2014 Guangzhou Proceedings",
  chapter="109594",
  howpublished="electronic, physical medium",
  year="2014",
  month="august",
  pages="1898--1901",
  type="conference paper"
}