Auto-biometric for M-mode echocardiography

In this paper we present a system for fast and accurate detection of anatomical structures (calipers) in M-mode images. The task is challenging because of dramatic variations in their appearances. We propose to solve the problem in a progressive manner, which ensures both robustness and efficiency. It first obtains rough caliper localization using the intensity profile image. Then run a constrained search for accurate caliper positions. Markov Random Field (MRF) and warping image detectors are used for jointly considering appearance information and the geometric relationship between calipers. Extensive experiments show that our system achieves more accurate results and uses less time in comparison with previously reported work.

[1]  Dorin Comaniciu,et al.  Fast Automatic Heart Chamber Segmentation from 3D CT Data Using Marginal Space Learning and Steerable Features , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[2]  Zhuowen Tu,et al.  Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[3]  Stan Z. Li,et al.  Markov Random Field Modeling in Computer Vision , 1995, Computer Science Workbench.

[4]  Gustavo Carneiro,et al.  A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure , 2007, 2007 IEEE 11th International Conference on Computer Vision.