Ovarian ultrasound image analysis: follicle segmentation

A Krivanek; M Sonka

doi:10.1109/42.746626

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Ovarian ultrasound image analysis: follicle segmentation

Journal article

Peer reviewed

Ovarian ultrasound image analysis: follicle segmentation

A Krivanek and M Sonka

IEEE transactions on medical imaging, Vol.17(6), pp.935-944

12/1998

DOI: 10.1109/42.746626

PMID: 10048850

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Abstract

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.

Image Processing, Computer-Assisted - statistics & numerical data

Ultrasonography - instrumentation

Ultrasonography - statistics & numerical data

Algorithms

Humans

Image Processing, Computer-Assisted - instrumentation

Female

Linear Models

Image Processing, Computer-Assisted - methods

Ovarian Follicle - diagnostic imaging

Ultrasonography - methods

Details

Title: Subtitle: Ovarian ultrasound image analysis: follicle segmentation
Creators: A Krivanek - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City 52242, USA
M Sonka
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.17(6), pp.935-944
DOI: 10.1109/42.746626
PMID: 10048850
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers; United States
Language: English
Date published: 12/1998
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984047994102771

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