Knee-joint tissue recognition in magnetic resonance imaging

Artjoms Suponenkovs; Zigurds Markovics; Ardis Platkajis

doi:10.1109/NC.2017.8263280

Knee-joint tissue recognition in magnetic resonance imaging

Artjoms Suponenkovs, Zigurds Markovics, Ardis Platkajis

Department of Radiology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The automatic knee-joint soft tissue recognition problem is very relevant due to increasing number of people with knee-joint diseases. It is for this reason that this paper investigates the problem of soft tissue recognition in magnetic resonance imaging (MRI). MRI is useful for knee-joint soft tissue presentation, but usually a doctor cannot see all necessary information in MRI data. Computer MRI analysis makes it possible to process all MRI data and shows additional information for the doctor. This additional information can make it easier to detect invisible injuries of knee-joint soft tissues. Knee-joint soft tissue recognition and analysis are very helpful, especially for osteoarthritis (OA) early diagnostics. Computer OA diagnostics are impossible without segmentation of knee-joint tissues. This publication describes approaches for knee-joint image pre-processing, knee-joint image segmentation, tissue recognition and tissue analysis. To solve tissue analysis task it is important to use biological information of knee-joint structure, physical and biochemical tissue features. Tissue analysis is very useful especially for early diagnostics. It allows starting treatment earlier and therefore reducing the risk of tissue destruction. It is for this reason that this paper investigates the above-mentioned challenges.

Original language	English
Title of host publication	IEEE 30th Jubilee Neumann Colloquium, NC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	41-46
Number of pages	6
ISBN (Electronic)	9781538646366
DOIs	https://doi.org/10.1109/NC.2017.8263280
Publication status	Published - 17 Jan 2018
Event	30th IEEE Jubilee Neumann Colloquium - Budapest, Hungary Duration: 24 Nov 2017 → 25 Nov 2017 Conference number: 30

Publication series

Name	IEEE 30th Jubilee Neumann Colloquium, NC 2017
Volume	2018-January

Conference

Conference	30th IEEE Jubilee Neumann Colloquium
Abbreviated title	NC 2017
Country/Territory	Hungary
City	Budapest
Period	24/11/17 → 25/11/17

Keywords*

co-occurrence matrix
computer vision
DICOM
image pre-processing
image segmentation
knee-joint
magnetic resonance imaging
medical imaging
osteoarthritis
tissue recognition

Field of Science*

3.2 Clinical medicine

Publication Type*

3.1. Articles or chapters in proceedings/scientific books indexed in Web of Science and/or Scopus database

Access to Document

10.1109/NC.2017.8263280

Cite this

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title = "Knee-joint tissue recognition in magnetic resonance imaging",

abstract = "The automatic knee-joint soft tissue recognition problem is very relevant due to increasing number of people with knee-joint diseases. It is for this reason that this paper investigates the problem of soft tissue recognition in magnetic resonance imaging (MRI). MRI is useful for knee-joint soft tissue presentation, but usually a doctor cannot see all necessary information in MRI data. Computer MRI analysis makes it possible to process all MRI data and shows additional information for the doctor. This additional information can make it easier to detect invisible injuries of knee-joint soft tissues. Knee-joint soft tissue recognition and analysis are very helpful, especially for osteoarthritis (OA) early diagnostics. Computer OA diagnostics are impossible without segmentation of knee-joint tissues. This publication describes approaches for knee-joint image pre-processing, knee-joint image segmentation, tissue recognition and tissue analysis. To solve tissue analysis task it is important to use biological information of knee-joint structure, physical and biochemical tissue features. Tissue analysis is very useful especially for early diagnostics. It allows starting treatment earlier and therefore reducing the risk of tissue destruction. It is for this reason that this paper investigates the above-mentioned challenges.",

keywords = "co-occurrence matrix, computer vision, DICOM, image pre-processing, image segmentation, knee-joint, magnetic resonance imaging, medical imaging, osteoarthritis, tissue recognition",

author = "Artjoms Suponenkovs and Zigurds Markovics and Ardis Platkajis",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 30th IEEE Jubilee Neumann Colloquium, NC 2017 ; Conference date: 24-11-2017 Through 25-11-2017",

year = "2018",

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doi = "10.1109/NC.2017.8263280",

language = "English",

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Suponenkovs, A, Markovics, Z & Platkajis, A 2018, Knee-joint tissue recognition in magnetic resonance imaging. in IEEE 30th Jubilee Neumann Colloquium, NC 2017. IEEE 30th Jubilee Neumann Colloquium, NC 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 41-46, 30th IEEE Jubilee Neumann Colloquium, Budapest, Hungary, 24/11/17. https://doi.org/10.1109/NC.2017.8263280

Knee-joint tissue recognition in magnetic resonance imaging. / Suponenkovs, Artjoms; Markovics, Zigurds; Platkajis, Ardis.
IEEE 30th Jubilee Neumann Colloquium, NC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 41-46 (IEEE 30th Jubilee Neumann Colloquium, NC 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Suponenkovs, Artjoms

AU - Markovics, Zigurds

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Y1 - 2018/1/17

N2 - The automatic knee-joint soft tissue recognition problem is very relevant due to increasing number of people with knee-joint diseases. It is for this reason that this paper investigates the problem of soft tissue recognition in magnetic resonance imaging (MRI). MRI is useful for knee-joint soft tissue presentation, but usually a doctor cannot see all necessary information in MRI data. Computer MRI analysis makes it possible to process all MRI data and shows additional information for the doctor. This additional information can make it easier to detect invisible injuries of knee-joint soft tissues. Knee-joint soft tissue recognition and analysis are very helpful, especially for osteoarthritis (OA) early diagnostics. Computer OA diagnostics are impossible without segmentation of knee-joint tissues. This publication describes approaches for knee-joint image pre-processing, knee-joint image segmentation, tissue recognition and tissue analysis. To solve tissue analysis task it is important to use biological information of knee-joint structure, physical and biochemical tissue features. Tissue analysis is very useful especially for early diagnostics. It allows starting treatment earlier and therefore reducing the risk of tissue destruction. It is for this reason that this paper investigates the above-mentioned challenges.

AB - The automatic knee-joint soft tissue recognition problem is very relevant due to increasing number of people with knee-joint diseases. It is for this reason that this paper investigates the problem of soft tissue recognition in magnetic resonance imaging (MRI). MRI is useful for knee-joint soft tissue presentation, but usually a doctor cannot see all necessary information in MRI data. Computer MRI analysis makes it possible to process all MRI data and shows additional information for the doctor. This additional information can make it easier to detect invisible injuries of knee-joint soft tissues. Knee-joint soft tissue recognition and analysis are very helpful, especially for osteoarthritis (OA) early diagnostics. Computer OA diagnostics are impossible without segmentation of knee-joint tissues. This publication describes approaches for knee-joint image pre-processing, knee-joint image segmentation, tissue recognition and tissue analysis. To solve tissue analysis task it is important to use biological information of knee-joint structure, physical and biochemical tissue features. Tissue analysis is very useful especially for early diagnostics. It allows starting treatment earlier and therefore reducing the risk of tissue destruction. It is for this reason that this paper investigates the above-mentioned challenges.

KW - co-occurrence matrix

KW - computer vision

KW - DICOM

KW - image pre-processing

KW - image segmentation

KW - knee-joint

KW - magnetic resonance imaging

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BT - IEEE 30th Jubilee Neumann Colloquium, NC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 30th IEEE Jubilee Neumann Colloquium

Y2 - 24 November 2017 through 25 November 2017

ER -

Knee-joint tissue recognition in magnetic resonance imaging

Abstract

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Keywords*

Field of Science*

Publication Type*

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