Segmentation of ultrasound fetal images

Wei Lu; Jinglu Tan

doi:10.1117/12.411742

29 December 2000 Segmentation of ultrasound fetal images

Wei Lu, Jinglu Tan

Proceedings Volume 4203, Biological Quality and Precision Agriculture II; (2000) https://doi.org/10.1117/12.411742
Event: Environmental and Industrial Sensing, 2000, Boston, MA, United States

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.

Citation Download Citation

Wei Lu and Jinglu Tan "Segmentation of ultrasound fetal images", Proc. SPIE 4203, Biological Quality and Precision Agriculture II, (29 December 2000); https://doi.org/10.1117/12.411742

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