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Proceedings Paper

Segmentation of ovarian follicles using geometric properties, texture descriptions, and boundary information
Author(s): Glynn P. Robinson; Amit Chakraborty; Michael Johnston; M. Lynne Reuss; James S. Duncan
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Paper Abstract

The size and number of follicles present within an ovary may be used as an indicator of fertility in women. Ultrasound is the imaging modality of choice for obtaining information on the follicles as it is inexpensive and readily available. A method of segmenting the follicles and ovary and producing accurate 2D and 3D representation would be of great benefit to a large segment of the population. However, the nature of ultrasound images means that standard approaches to segmentation based on image gradients or detecting regions of homogeneous gray-level alone are inadequate. A semi-automatic method of segmentation which combined a texture based classification for initial segmentation with deformable models to provide descriptions of individual objects is extended by imposing geometric constraints on the relationships between the individual objects present within an image. Since we are interested in segmenting the individual objects over a 3D spatial stack we use the results from one image in the sequence as the initial estimates for the next image. This reduces the need for operator intervention and provides representations of individual objects through the whole sequence. These representations can then be used for accurate measurement of area/volume and for three-dimensional visualization of the relationships between the individual follicles and the enclosing ovary.

Paper Details

Date Published: 16 April 1996
PDF: 10 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237935
Show Author Affiliations
Glynn P. Robinson, Yale Univ. Medical School (United States)
Amit Chakraborty, Yale Univ. Medical School (United States)
Michael Johnston, Belmont Research Inc. (United States)
M. Lynne Reuss, Columbia Univ. Medical Ctr. (United States)
James S. Duncan, Yale Univ. Medical School (United States)

Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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