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Proceedings Paper

Segmentation of ultrasound fetal images
Author(s): Wei Lu; Jinglu Tan
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Paper Abstract

Segmentation of ultrasound images is challenging because of the noisy nature and subtle boundaries of objects in ultrasound images. This paper discusses object segmentation and identification for ultrasound fetal images. The feature space for segmentation consists of information extracted from three sources: gray level, texture, and wavelet-based decomposition. Several texture features, including Laws' texture-energy measures and features based on local gray level run-length, were found useful for segmentation. An unsupervised clustering procedure was used to classify each pixel into its most probable class. Morphological operations were used to remove noisy structures from the original gray level images and to improve the boundaries of the segmented objects. An algorithm was developed to locate objects of interest based on a multiscale implementation of an image transform. Fetal heads were identified and their corresponding measurements are made automatically. The method was tested with a set of clinical images. The resulting images showed clearly the segmented objects. The measurements agreed closely with a sonographer's measurements. The purposed method holds promise for processing and analyzing ultrasound fetal images.

Paper Details

Date Published: 29 December 2000
PDF: 10 pages
Proc. SPIE 4203, Biological Quality and Precision Agriculture II, (29 December 2000); doi: 10.1117/12.411742
Show Author Affiliations
Wei Lu, Univ. of Missouri/Columbia (United States)
Jinglu Tan, Univ. of Missouri/Columbia (United States)

Published in SPIE Proceedings Vol. 4203:
Biological Quality and Precision Agriculture II
James A. DeShazer; George E. Meyer, Editor(s)

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