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.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Krivanek A,Sonka Mdoi
10.1109/42.746626subject
Has Abstractpub_date
1998-12-01 00:00:00pages
935-44issue
6eissn
0278-0062issn
1558-254Xjournal_volume
17pub_type
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journal_title:IEEE transactions on medical imaging
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journal_title:IEEE transactions on medical imaging
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journal_title:IEEE transactions on medical imaging
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journal_title:IEEE transactions on medical imaging
pub_type: 杂志文章
doi:10.1109/42.746730
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journal_title:IEEE transactions on medical imaging
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journal_title:IEEE transactions on medical imaging
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