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dc.contributor.advisorEramian, Mark G.en_US
dc.creatorHarrington, Naen_US
dc.date.accessioned2007-09-12T16:48:22Zen_US
dc.date.accessioned2013-01-04T04:57:29Z
dc.date.available2008-09-14T08:00:00Zen_US
dc.date.available2013-01-04T04:57:29Z
dc.date.created2007en_US
dc.date.issued2007en_US
dc.date.submitted2007en_US
dc.identifier.urihttp://hdl.handle.net/10388/etd-09122007-164822en_US
dc.description.abstractOvarian follicles are spherical structures inside the ovaries which contain developing eggs. Monitoring the development of follicles is necessary for both gynecological medicine (ovarian diseases diagnosis and infertility treatment), and veterinary medicine (determining when to introduce superstimulation in cattle, or dividing herds into different stages in the estrous cycle).Ultrasound imaging provides a non-invasive method for monitoring follicles. However, manually detecting follicles from ovarian ultrasound images is time consuming and sensitive to the observer's experience. Existing (semi-) automatic follicle segmentation techniques show the power of automation, but are not widely used due to their limited success.A new automated follicle segmentation method is introduced in this thesis. Human ovarian images acquired in vivo were smoothed using an adaptive neighbourhood median filter. Dark regions were initially segmented using geometric active contour models. Only part of these segmented dark regions were true follicles. A naïve Bayes classifier was applied to determine whether each segmented dark region was a true follicle or not. The Hausdorff distance between contours of the automatically segmented regions and the gold standard was 2.43 ± 1.46 mm per follicle, and the average root mean square distance per follicle was 0.86 ± 0.49 mm. Both the average Hausdorff distance and the root mean square distance were larger than those reported in other follicle segmentation algorithms. The mean absolute distance between contours of the automatically segmented regions and the gold standard was 0.75 ± 0.32 mm, which was below that reported in other follicle segmentation algorithms.The overall follicle recognition rate was 33% to 35%; and the overall image misidentification rate was 23% to 33%. If only follicles with diameter greater than or equal to 3 mm were considered, the follicle recognition rate increased to 60% to 63%, and the follicle misidentification rate increased slightly to 24% to 34%. The proposed follicle segmentation method is proved to be accurate in detecting a large number of follicles with diameter greater than or equal to 3 mm.en_US
dc.language.isoen_USen_US
dc.subjectNaïve Bayes Classifieren_US
dc.subjectUltrasound Imagesen_US
dc.subjectFollicle Segmentationen_US
dc.subjectGeometric Active Contour Modelsen_US
dc.titleSegmentation of human ovarian follicles from ultrasound images acquired in vivo using geometric active contour models and a naïve Bayes classifieren_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US
dc.contributor.committeeMemberPierson, Roger A.en_US
dc.contributor.committeeMemberNeufeld, Ericen_US


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