Ovarian ultrasound image analysis: follicle segmentation.
Ovarian ultrasound is an effective tool in infertility treatment. Repeated measurements of the size and shape of follicles over several days are the primary means of evaluation by physicians. Currently, follicle wall segmentation is achieved by manual tracing which is time consuming and susceptible to inter-operator variation. An automated method for follicle wall segmentation is reported that uses a four-step process based on watershed segmentation and knowledge-based graph search algorithm which utilizes priori information about follicle structure for inner and outer wall detection. The automated technique was tested on 36 ultrasonographic images of women's ovaries. Validation against manually traced borders has shown good correlation of manually defined and computer-determined area measurements (R2 = 0.85 - 0.96). The border positioning errors were small: 0.63+/-0.36 mm for inner border and 0.67+/-0.41 mm for outer border detection. The use of watershed segmentation and graph search methods facilitates fast, accurate inner and outer border detection with minimal user-interaction.
Department of Electrical and Computer Engineering, The University of Iowa, Iowa City 52242, USA.
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Krivanek
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Sonka
D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D016014:Linear Models; D006080:Ovarian Follicle / Q000000981:diagnostic imaging*; D014463:Ultrasonography / Q000295:instrumentation / Q000379:methods / Q000706:statistics & numerical data