Author : Jiyo.S.Athertya
Affiliation : Department of Engineering Design, IIT-Madras
Country : India
Category : Computer Science & Information Technology
Volume, Issue,
Month, Year
: 6, 4, April, 2016
ABSTRACT
Computerized
medical image segmentation is a challenging area because of poor resolution and
weak contrast. The predominantly used conventional clustering techniques and
the thresholding methods suffer from limitations owing to their heavy dependence
on user interactions. Uncertainties prevalent in an image cannot be captured by
these techniques. The performance further deteriorates when the images are
corrupted by noise, outliers and other artifacts. The objective of this paper
is to develop an effective robust fuzzy C- means clustering for segmenting
vertebral body from magnetic resonance images. The motivation for this work is
that spine appearance, shape and geometry measurements are necessary for
abnormality detection and thus proper localisation and labelling will enhance
the diagnostic output of a physician. The method is compared with Otsu
thresholding and K-means clustering to illustrate the robustness. The reference
standard for validation was the annotated images from the radiologist, and the
Dice coefficient and Hausdorff distance measures were used to evaluate the
segmentation.
Keyword : Vertebra segmentation, fuzzy clustering, MRI,
labelling
For More Details :
https://airccj.org/CSCP/vol6/csit65109.pdf
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