A multiparametric and multiresolution segmentation algorithm of 3-D ultrasonic data.
An algorithm devoted to the segmentation of 3-D ultrasonic data is proposed. The algorithm involves 3-D adaptive clustering based on multiparametric information: the gray-scale intensity of the echographic data, 3-D texture features calculated from the envelope data, and 3-D tissue characterization information calculated from the local frequency spectra of the radio-frequency signals. The segmentation problem is formulated as a Maximum A posterior (MAP) estimation problem. A multi-resolution implementation of the algorithm is proposed. The approach is tested on simulated data and on in vivo echocardiographic 3-D data. The results presented in the paper illustrate the robustness and the accuracy of the proposed approach for the segmentation of ultrasonic data.
Boukerroui
D
D
CREATIS, CNRS Research Unit (UMR 5515), Villeurbanne, France.
Basset
O
O
Baskurt
A
A
Gimenez
G
G
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004285:Dogs; D019560:Echocardiography, Three-Dimensional / Q000379:methods; D006321:Heart / Q000033:anatomy & histology; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D019047:Phantoms, Imaging; D014463:Ultrasonography / Q000379:methods*