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Structural Brain MRI Segmentation Using Machine Learning Technique
KTH, School of Technology and Health (STH). (Medical Imaging and Visualization Group)
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Segmenting brain MR scans could be highly benecial for diagnosing, treating and evaluating the progress of specic diseases. Up to this point, manual segmentation,performed by experts, is the conventional method in hospitals and clinical environments. Although manual segmentation is accurate, it is time consuming, expensive and might not be reliable. Many non-automatic and semi automatic methods have been proposed in the literature in order to segment MR brain images, but the levelof accuracy is not comparable with manual segmentation.

The aim of this project is to implement and make a preliminary evaluation of a method based on machine learning technique for segmenting gray matter (GM),white matter (WM) and cerebrospinal uid (CSF) of brain MR scans using images available within the open MICCAI grand challenge (MRBrainS13).The proposed method employs supervised articial neural network based autocontext algorithm, exploiting intensity-based, spatial-based and shape model-basedlevel set segmentation results as features of the network. The obtained average results based on Dice similarity index were 97.73%, 95.37%, 82.76%, 88.47% and 84.78% for intracranial volume, brain (WM + GM), CSF, WM and GM respectively. This method achieved competitive results with considerably shorter required training time in MRBrainsS13 challenge.

Place, publisher, year, edition, pages
2016. , 50 p.
TRITA-STH, 2016:78
Keyword [en]
MRI, Machine Learning, Brain Image Segmentation
National Category
Medical Engineering
URN: urn:nbn:se:kth:diva-189985OAI: diva2:949994
Subject / course
Medical Engineering
Educational program
Master of Science - Medical Engineering
2016-06-09, 3-221, Alfred Nobels Allé 10, Huddinge, 16:00 (English)
Available from: 2016-08-01 Created: 2016-07-26 Last updated: 2016-08-01Bibliographically approved

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