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      Evaluation of texture features for analysis of ovarian follicular development

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      Date
      2005-09-30
      Author
      Bian, Na
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      Ovarian follicles in women are fluid-filled structures in the ovary that contain oocytes (eggs). A dominant follicle is physiologically selected and ovulates during the menstrual cycle. We examined the echotexture in ultrasonographic images of the follicle wall of dominant ovulatory follicles in women during natural menstrual cycles and dominant anovulatory follicles which developed in women using oral contraceptives (OC). Texture features of follicle wall regions of both ovulatory and anovulatory dominant follicles were evaluated over a period of seven days before ovulation (natural cycles) or peak estradiol concentrations (OC cycles). Differences in echotexture between the two classes of follicles were found for two co-occurrence matrix derived texture features and two edge-frequency based texture features. Co-occurrence energy and homogeneity were significantly lower for ovulatory follicles while edge density and edge contrast were higher for ovulatory follicles. In the each feature space, the two classes of follicle were adequately separable.This thesis employed several statistical approaches to analyses of texture features, such as plotting method and the Mann-Kendall method. A distinct change of feature trend was detected 3 or 4 days before the day of ovulation for ovulatory follicles in the two co-occurrence matrix derived texture features and two edge-frequency-based texture features. Anovulatory follicles, exhibited the biggest variation of the feature value 3 or 4 days before the day on which dominant follicles developed to maximum size. This discovery is believed to correspond to the ovarian follicles responding to system hormonal changes leading to presumptive ovulation.
      Degree
      Master of Science (M.Sc.)
      Department
      Computer Science
      Program
      Computer Science
      Supervisor
      Eramian, Mark G.
      Committee
      Pierson, Roger A.; Horsch, Michael C.; Adams, Gregg P.
      Copyright Date
      September 2005
      URI
      http://hdl.handle.net/10388/etd-12022005-114421
      Subject
      trend analysis
      ovarian follicle
      texture analysis
      feature extraction
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