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Optimal surface detection in intravascular ultrasound using multi-dimensional graph search

A new algorithm is proposed for the detection of optimal surfaces in multi-dimensional datasets. The algorithm is based on multi-dimensional dynamic programming. Cost functions were derived using a cylindrical object model by resampling the raw image data along perpendiculars to a prior contour. Radial and angular smoothing filters were used together with a radial derivative operator to convert the data into a cost function. The algorithm was applied To IVUS image sequences of explanted peripheral arterial segments. The algorithm was assessed by comparing the arterial wall estimates obtained from tracings, obtained slice by slice, by an expert human. The slope and intercept were: 1.06/spl plusmn/0.05, -57/spl plusmn/255 pixels, respectively. The slope and intercepts were not different from unity and zero, respectively (p>0.2). The algorithm is capable of recovering surfaces in 3D, has fixed memory requirements, and is fast.