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Driving innovations in manufacturing: Open Access research from DMEM

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Design, Manufacture & Engineering Management (DMEM).

Centred on the vision of 'Delivering Total Engineering', DMEM is a centre for excellence in the processes, systems and technologies needed to support and enable engineering from concept to remanufacture. From user-centred design to sustainable design, from manufacturing operations to remanufacturing, from advanced materials research to systems engineering.

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Fuzzy weighted-offset multi-scale edge detection for automatic echocardiographic lV boundary extraction

Sheikh Akbari, A. and Soraghan, J.J. (2002) Fuzzy weighted-offset multi-scale edge detection for automatic echocardiographic lV boundary extraction. In: IEE Seminar Medical Applications of Signal Processing, 2002-10-07 - 2002-10-07.

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Abstract

This paper describes a new Fuzzy, Weighted-Offset, Multiscale Edge Detection algorithm for cardiac left ventricular (LV) epicardial and endocardial boundary detection on short axis (SA) echocardiographic images. The proposed method uses the `centre-based' approach, previously described in S.K. Setarehdan and J.J. Soraghan, 1999, IEEE Transaction on Biomedical Engineering. Vol. 46, No. 11, 1364 - 1378. The Edge-detection stage uses a new Fuzzy Weighted Offset Multiscale Edge Detection (FWOMED) technique in order to identify a single moving point for each one of the epicardial and endocardial boundaries over the N radii in an echocardiographic frame. This technique achieves optimal edge detection through non-decimated wavelet decomposition of the original signal followed by a fuzzy based decision technique, which is applied across the scales. Finally, a uniform cubic B-spline approximation is used to define the closed LV boundaries. The performance of this technique is compared to Mallat's (S. Mallat and S. Zhong, 1992, IEEE Trans. PAMI, Vol. 14, No. 7, 710-723.) multiscale edge detection technique, for a range of test data sets comprising different synthetic noisy signals.