Polycystic ovary syndrome (PCOS) is an endocrine abnormality with multiple diagnostic criteria due to its heterogenic manifestations. One of the diagnostic criterion includes analysis of ultrasound images of ovaries for the detection of number, size, and distribution of follicles within the ovary. This involves manual tracing of follicles on the ultrasound images to determine the presence of a polycystic ovary (PCO). A novel method that automates PCO morphology detection is described. Our algorithm involves automatic segmentation of follicles from ultrasound images, quantifying the attributes of the segmented
follicles using stereology, storing follicle attributes as feature vectors, and finally
classification of the feature vector into two categories. The classification categories are
PCO morphology present and PCO morphology absent. An automatic PCO diagnostic tool would save considerable time spent on manual tracing of follicles and measuring the length and width of every follicle. Our procedure was able to achieve classification accuracy of 92.86% using a linear discriminant classifier. Our classifier will improve the rapidity and accuracy of PCOS diagnosis, and reduce the chance of the severe health implications that can arise from delayed diagnosis.
Identifer | oai:union.ndltd.org:USASK/oai:usask.ca:etd-08252008-222010 |
Date | 29 August 2008 |
Creators | Raghavan, Mary Ruth Pradeepa |
Contributors | Sarty, Gordon E., Pierson, Roger A., Neufeld, Eric, Eramian, Mark G., Chen, X. B. (Daniel), Singh, Jaswant |
Publisher | University of Saskatchewan |
Source Sets | University of Saskatchewan Library |
Language | English |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | http://library.usask.ca/theses/available/etd-08252008-222010/ |
Rights | restricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Saskatchewan or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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