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Grant: Discovery Grants Program - Individual

Laurentian University of Sudbury

$14,000.00

Image segmentation and understanding

FY 2012-13

Summary
Medical imaging is an important tool that allows physicians to look inside human bodies. It may detect critical illnesses at their most curable stage and, in many cases, when they are least costly to treat. Currently, various image modalities (e.g. ultrasound, Computer Tomography CT, Magnetic Resonance Imaging MRI, etc) are heavily used for clinical purposes. Ultrasound imaging provides an attractive tool for carotid artery examination. It allows clinicians to detect possible artery stenosis and to assess the risk of developing stokes. The main drawback of ultrasound imaging is the poor quality of the produced images. It takes a considerable effort from clinicians to extract significant information about carotid artery contours, including the possible existence of plaque layers that may exist. This task requires a highly skilled clinician. Furthermore, manual extraction of carotid artery contours generates a result that is not reproducible. The main objective of this research is to develop a Computer Aided Diagnostic (CAD) system to analyze carotid artery ultrasound images in an attempt to detect carotid artery stenosis and to assess the risk of developing strokes. The ultimate goal is to promote the use of the proposed system in clinical trials.
Program
Discovery Grants Program - Individual
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
371959-2011
Name nom
AbdelDayem, Amr
Department département
Mathematics and Computer Science
Organization id
26
Institution établissement
Laurentian University of Sudbury
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2012
Competition year année de concours
2011
Award amount
14000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
1102
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Biomedical engineering
Area of application fr
Génie biomédical
Research subject code
2603
Research subject group en
Robotics
Research subject group fr
Robotique
Research subject en
Computer vision
Research subject fr
Vision artificielle
Partie
2
Nb partie
5
Application title
Image segmentation and understanding
Keyword
NSERC
Application summary
Medical imaging is an important tool that allows physicians to look inside human bodies. It may detect critical illnesses at their most curable stage and, in many cases, when they are least costly to treat. Currently, various image modalities (e.g. ultrasound, Computer Tomography CT, Magnetic Resonance Imaging MRI, etc) are heavily used for clinical purposes. Ultrasound imaging provides an attractive tool for carotid artery examination. It allows clinicians to detect possible artery stenosis and to assess the risk of developing stokes. The main drawback of ultrasound imaging is the poor quality of the produced images. It takes a considerable effort from clinicians to extract significant information about carotid artery contours, including the possible existence of plaque layers that may exist. This task requires a highly skilled clinician. Furthermore, manual extraction of carotid artery contours generates a result that is not reproducible. The main objective of this research is to develop a Computer Aided Diagnostic (CAD) system to analyze carotid artery ultrasound images in an attempt to detect carotid artery stenosis and to assess the risk of developing strokes. The ultimate goal is to promote the use of the proposed system in clinical trials.
Laurentian University of Sudbury - $14,000 Grant (201… | Canada Spends