Improved edge detection for object segmentation in ultrasound images using Active Shape Models

F. Arámbula Cosío; Hèctor G. Acosta; Edgar Conde

doi:10.1117/12.2070559

28 January 2015 Improved edge detection for object segmentation in ultrasound images using Active Shape Models

F. Arámbula Cosío, Hèctor G. Acosta, Edgar Conde

Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 928714 (2015) https://doi.org/10.1117/12.2070559
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia

Abstract

We report a new method for adjusting the points of an active shape model (ASM) to the edge of an object, on a grey level image. The method is based on the original iterative search for an optimum location of each point of the ASM, along the normal direction to the model boundary. In this work we determine the optimum location of the model boundary point through minimization of the error (euclidean distance) between a profile of pixels sampled along the normal direction, and its projection on the principal component axes, obtained from a training set of normal pixel profiles, located at the edge of the object. We validated our method on ultrasound images of the prostate and photographs of the left hand. Significant improvements were observed in the segmentation of the ultrasound images, with reference to the original ASM method of adjustment, while no significant improvement was observed for the photographs. Our method produced a mean error of 4.58 (mm) between corresponding expert and automatically annotated contours of the ultrasound images of the prostate, and 3.12 (mm) of mean error for the photographs of the left hand.

Citation Download Citation

F. Arámbula Cosío, Hèctor G. Acosta, and Edgar Conde "Improved edge detection for object segmentation in ultrasound images using Active Shape Models", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928714 (28 January 2015); https://doi.org/10.1117/12.2070559

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