Applications of Computer Vision, IEEE Workshop on (2002)
Dec. 3, 2002 to Dec. 4, 2002
Nualsawat Hiransakolwong , University of Central Florida
Piotr S. Windyga , University of Central Florida
Kien A. Hua , University of Central Florida
Khanh Vu , Oklahoma State University
In this paper, we propose a novel segmenting system for ultrasound images. This solution is separated into three steps. First, we filter noise by using the "peak-and- valley" with scanning pixels along the Hilbert curve. Then we use the "Cubic Spline Interpolation" between local peaks and valleys to smooth the image. Second, we present windows adaptive threshold, to eliminate trial and error, as the method for obtaining the right threshold for beginning segmentation. Third, we label distinct, disconnected objects and use our "core area" to detect the object of interest based on the feature knowledge bases. Our method was experimented with liver ultrasound images. We compared the orientation and centroid feature vectors of our Full Automatic Segmenting Ultrasound (FASU) method with the manual segmentation method. The results are fully automatic and confirm the accuracy of our FASU method.
Ultrasound image segmentation, Image smoothing, peak-and-valley, Hilbert curve, windows adaptive threshold, core area, Cubic Spline Interpolation
P. S. Windyga, N. Hiransakolwong, K. A. Hua and K. Vu, "FASU: A Full Automatic Segmenting System for Ultrasound Images," Applications of Computer Vision, IEEE Workshop on(WACV), Orlando, Florida, 2002, pp. 90.