Semi-automatic corpus callosum segmentation and 3D visualization using active contour methods

Marcin Ciecholewski , Jan H. Spodnik


Accurate 3D computer models of the brain, and also of parts of its structure such as the corpus callosum (CC) are increasingly used in routine clinical diagnostics. This study presents comparative research to assess the utility and performance of three active contour methods (ACMs) for segmenting the CC from magnetic resonance (MR) images of the brain, namely: an edge-based active contour model using an inflation/deflation force with a damping coefficient (EM), the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method and the Distance Regularized Level Set Evolution (DRLSE) method. The pre-processing methods applied during research work were to improve the contrast, reduce noise and thus help segment the CC better. In this project, 3D CC models reconstructed based on the segmentations of cross-sections of MR images were also visualised. The results, as measured by quantitative tests of the similarity indice (SI) and overlap value (OV) are the best for the EM model (SI = 92%, OV = 82%) and are comparable to or better than those for other methods taken from a literature review. Furthermore, the properties of the EM model consisting in its ability to both expand and shrink at the same time allow segmentations to be better fitted in subsequent CC slices then in state-of-the art ACMs such as DRLSE or SBGFRLS. The CC contours from previous and subsequent iterations produced by the EM model can be used for initiation in subsequent or previous frames of MR images, which makes the segmentation process easier, particularly as the CC area can increase or decrease in subsequent MR image frames.
Author Marcin Ciecholewski (FMPI / II)
Marcin Ciecholewski,,
- Institute of Informatics
, Jan H. Spodnik
Jan H. Spodnik,,
Journal seriesSymmetry-Basel, ISSN 2073-8994, (A 30 pkt)
Issue year2018
Publication size in sheets1.2
Keywords in Englishactive contour, edge-based active contour, region-based active contour, image processing, segmentation, 3d visualisation, magnetic resonance imaging, corpus callosum, Alzheimer’s disease
ASJC Classification3101 Physics and Astronomy (miscellaneous); 2600 General Mathematics; 1601 Chemistry (miscellaneous); 1701 Computer Science (miscellaneous)
Languageen angielski
LicenseJournal (articles only); published final; Uznanie Autorstwa (CC-BY); with publication
Score (nominal)30
ScoreMinisterial score = 30.0, 24-07-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2016 = 0.64; WoS Impact Factor: 2017 = 1.256 (2) - 2017=1.213 (5)
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