Edge detection in ultrasound imagery using the instantaneous coefficient of variation

• Published in: IEEE transactions on image processing Vol. 13; no. 12; pp. 1640 - 1655
• Main Authors: Yongjian Yu, Acton, S.T.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anisotropic magnetoresistance; Applied sciences; Artificial Intelligence; Edge detection; Exact sciences and technology; Filters; Gamma ray detection; Gamma ray detectors; Humans; Image edge detection; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Information Storage and Retrieval - methods; Information, signal and communications theory; instantaneous coefficient of variation; Laplace equations; Models, Biological; Models, Statistical; Numerical Analysis, Computer-Assisted; Pattern recognition; Pattern Recognition, Automated - methods; Phantoms, Imaging; Probability; Reproducibility of Results; Sensitivity and Specificity; Signal processing; Signal Processing, Computer-Assisted; Speckle; Studies; Telecommunications and information theory; ultrasonic image; Ultrasonic imaging; Ultrasonography - instrumentation; Ultrasonography - methods; Performance evaluation; ultrasonic image; Image processing; Ultrasound imaging; Speckle; Algorithm; Shape detection; Implementation; Laplacian; False alarm rate; Diffusion process; Feature extraction; Anisotropic scattering; Diffusion coefficient; Acoustic image; Localization; instantaneous coefficient of variation; Edge detection; Gradient method
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2004.836166

The instantaneous coefficient of variation (ICOV) edge detector, based on normalized gradient and Laplacian operators, has been proposed for edge detection in ultrasound images. In this paper, the edge detection and localization performance of the ICOV-squared (ICOVS) detector are examined. First, a simplified version of the ICOVS detector, the normalized gradient magnitude squared, is scrutinized in order to reveal the statistical performance of edge detection and localization in speckled ultrasound imagery. Both the probability of detection and the probability of false alarm are evaluated for the detector. Edge localization is characterized by the position of the peak and the 3-dB width of the detector response. Then, the speckle-edge response of the ICOVS as applied to a realistic edge model is studied. Through theoretical analysis, we reveal the compensatory effects of the normalized Laplacian operator in the ICOV edge detector for edge-localization error. An ICOV-based edge-detection algorithm is implemented in which the ICOV detector is embedded in a diffusion coefficient in an anisotropic diffusion process. Experiments with real ultrasound images have shown that the proposed algorithm is effective in extracting edges in the presence of speckle. Quantitatively, the ICOVS provides a lower localization error, and qualitatively, a dramatic improvement in edge-detection performance over an existing edge-detection method for speckled imagery.