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      Computer assisted detection of polycystic ovary morphology in ultrasound images

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      Computer_Assisted_Detection_of_PCO_Morphology_in_US_Images.pdf (1.049Mb)
      Date
      2008
      Author
      Raghavan, Mary Ruth Pradeepa
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      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.
      Degree
      Master of Science (M.Sc.)
      Department
      Biomedical Engineering
      Program
      Biomedical Engineering
      Supervisor
      Eramian, Mark G.
      Committee
      Sarty, Gordon E.; Pierson, Roger A.; Neufeld, Eric; Chen, X. B. (Daniel); Singh, Jaswant
      Copyright Date
      2008
      URI
      http://hdl.handle.net/10388/etd-08252008-222010
      Subject
      Ultrasound images
      Polycystic ovary
      follicle segmentation
      stereology
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