We propose a robust and fully automatic algorithm which computes the 2D echocardiography measurements
recommended by America Society of Echocardiography. The algorithm employs knowledge-based imaging technologies
which can learn the expert's knowledge from the training images and expert's annotation. Based on
the models constructed from the learning stage, the algorithm searches initial location of the landmark points
for the measurements by utilizing heart structure of left ventricle including mitral valve aortic valve. It employs
the pseudo anatomic M-mode image generated by accumulating the line images in 2D parasternal long axis view
along the time to refine the measurement landmark points. The experiment results with large volume of data
show that the algorithm runs fast and is robust comparable to expert.
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.