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Proceedings Paper

Automated follicle analysis in ovarian ultrasound
Author(s): Anthony Krivanek; Weidong Liang; Gordon E. Sarty; Roger A. Pierson; Milan Sonka
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Paper Abstract

For women undergoing assisted reproductive therapy, ovarian ultrasound has become an invaluable tool for monitoring the growth and assessing the physiological status of individual follicles. Measurements of the size and shape of follicles 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. We are introducing a completely automated method of follicle wall isolation which provides faster, more consistent analysis. Our automated method is a 4-step process which employs watershed segmentation and a knowledge-based graph search algorithm which utilizes a priori information about follicle structure for inner and outer wall detection. The automated technique was tested on 36 ultrasonographic images of woman's ovaries. Five images from this set were omitted due to poor image quality. Validation of the remaining 31 ultrasound images against manually traced borders has shown an average rms error of 0.61 +/- 0.40 mm for inner border and 0.61 +/- 0.31 mm for outer border detection. Quantitative comparison of the computer-defined borders and the user-defined borders advocates the accuracy of our automated method of follicle analysis.

Paper Details

Date Published: 24 June 1998
PDF: 9 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310937
Show Author Affiliations
Anthony Krivanek, Univ. of Iowa (United States)
Weidong Liang, Univ. of Iowa (United States)
Gordon E. Sarty, Univ. of Saskatchewan College of Medicine (Canada)
Roger A. Pierson, Univ. of Saskatchewan College of Medicine (Canada)
Milan Sonka, Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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