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.
FASU: a Full Automatic Segmenting System for Ultrasound images
2002-01-01
576803 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
FASU: A Full Automatic Segmenting System for Ultrasound Images
British Library Conference Proceedings | 2002
|Segmenting Images for a Better Diagnosis
NTRS | 2004
An algorithm for segmenting cytological images
British Library Online Contents | 2008
|Active Region Models for Segmenting Medical Images
British Library Conference Proceedings | 1994
|Segmenting Non Stationary Images with Triplet Markov Fields
British Library Conference Proceedings | 2005
|