Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements Page: 2
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Kumara, Muthukudage Jayantha. Automated Real-time Objects Detection in
Colonoscopy Videos for Quality Measurements. Doctor of Philosophy (Computer Science and
Engineering), August 2013, 80 pp., 13 tables, 21 figures, bibliography, 75 titles.
The effectiveness of colonoscopy depends on the quality of the inspection of the colon.
There was no automated measurement method to evaluate the quality of the inspection. This
thesis addresses this issue by investigating an automated post-procedure quality measurement
technique and proposing a novel approach automatically deciding a percentage of stool areas in
images of digitized colonoscopy video files. It involves the classification of image pixels based
on their color features using a new method of planes on RGB (red, green and blue) color space.
The limitation of post-procedure quality measurement is that quality measurements are
available long after the procedure was done and the patient was released. A better approach is
to inform any sub-optimal inspection immediately so that the endoscopist can improve the
quality in real-time during the procedure. This thesis also proposes an extension to post-
procedure method to detect stool, bite-block, and blood regions in real-time using color
features in HSV color space. These three objects play a major role in quality measurements in
colonoscopy. The proposed method partitions very large positive examples of each of these
objects into a number of groups. These groups are formed by taking intersection of positive
examples with a hyper plane. This hyper plane is named as 'positive plane'. 'Convex hulls' are
used to model positive planes. Comparisons with traditional classifiers such as K-nearest
neighbor (K-NN) and support vector machines (SVM) proves the soundness of the proposed
method in terms of accuracy and speed that are critical in the targeted real-time qualitymeasurement system.
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Kumara, Muthukudage Jayantha. Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements, dissertation, August 2013; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc283843/m1/2/: accessed April 23, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .