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Automatic Detection of Anatomical Landmarks in Three-Dimensional MRI
Linköping University, Department of Electrical Engineering, Computer Vision.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Detection and positioning of anatomical landmarks, also called points of interest(POI), is often a concept of interest in medical image processing. Different measures or automatic image analyzes are often directly based upon positions of such points, e.g. in organ segmentation or tissue quantification. Manual positioning of these landmarks is a time consuming and resource demanding process. In this thesis, a general method for positioning of anatomical landmarks is outlined, implemented and evaluated. The evaluation of the method is limited to three different POI; left femur head, right femur head and vertebra T9. These POI are used to define the range of the abdomen in order to measure the amount of abdominal fat in 3D data acquired with quantitative magnetic resonance imaging (MRI). By getting more detailed information about the abdominal body fat composition, medical diagnoses can be issued with higher confidence. Examples of applications could be identifying patients with high risk of developing metabolic or catabolic disease and characterizing the effects of different interventions, i.e. training, bariatric surgery and medications. The proposed method is shown to be highly robust and accurate for positioning of left and right femur head. Due to insufficient performance regarding T9 detection, a modified method is proposed for T9 positioning. The modified method shows promises of accurate and repeatable results but has to be evaluated more extensively in order to draw further conclusions.

Place, publisher, year, edition, pages
2016. , 59 p.
Keyword [en]
Quantitative 3D MRI, Machine learning, Medical image processing, Morphon image registration, 3D Haar features, Random regression forests
National Category
Medical Image Processing
URN: urn:nbn:se:liu:diva-130944ISRN: LiTH-ISY-EX--16/4990--SEOAI: diva2:957048
External cooperation
Subject / course
Computer Vision Laboratory
Available from: 2016-09-07 Created: 2016-08-31 Last updated: 2016-09-07Bibliographically approved

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Järrendahl, Hannes
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