DocumentCode :
27774
Title :
Revamped fly-over for accurate colon visualisation in virtual colonoscopy
Author :
Ismail, Marwa ; Elshzaly, Salwa ; Farag, Aly ; Sites, Chuck ; Curtin, Robert ; Falk, Robert ; Seow, Albert ; Dryden, Gerald
Author_Institution :
Comput. Vision & Image Process. Lab., Univ. of Louisville, Louisville, KY, USA
Volume :
9
Issue :
4
fYear :
2015
fDate :
8 2015
Firstpage :
511
Lastpage :
521
Abstract :
This study revisits a visualisation technique for virtual colonoscopy, known as virtual fly-over (FO). The method views the entire colon anatomy from above the centreline. It assigns two cameras located on opposite sides of the centreline, each of which is responsible for viewing one half of the colon. This approach has several advantages over related colon visualisation methods with regards to visibility coverage and polyp detection rate. However, the traditional FO implementation created a few drawbacks that hinder complete visualisation. For example, it could overlook polyps located on the line between the two halves, and it has no data-specific initialisation for cutting planes. The authors enhance the FO method in a number of respects in this study: resolve the cutting issue and improve the virtual camera setup in order to better visualise both the colon surface´s hidden structures and polyps that are difficult to locate. Quantitative validation of the revamped FO on 30 actual clinical datasets with complicated shapes and large volumes demonstrated that the average surface visualisation rate is equal to 99.5 ± 0.2%. Also, true and synthetic polyps with various shapes and sizes were used to clinically validate the proposed method. Detection rate is up to 100% on the tested sets.
Keywords :
cameras; computerised tomography; medical image processing; clinical datasets; colon anatomy; colon visualisation methods; polyp detection rate; revamped fly-over; surface visualisation rate; synthetic polyps; traditional FO implementation; virtual camera setup; virtual colonoscopy; virtual fly-over;
fLanguage :
English
Journal_Title :
Computer Vision, IET
Publisher :
iet
ISSN :
1751-9632
Type :
jour
DOI :
10.1049/iet-cvi.2014.0177
Filename :
7172635
Link To Document :
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