The synergy of 3D SIFT and sparse codes for classification of viewpoints from echocardiogram videos

Qian, Yu, Wang, Lianyi, Wang, Chunyan and Gao, Xiaohong W. (2013) The synergy of 3D SIFT and sparse codes for classification of viewpoints from echocardiogram videos. In: Medical Content-Based Retrieval for Clinical Decision Support. Lecture Notes In Computer Science, 7723 . Springer-Verlag, Berlin, pp. 68-79. 9783642366772. (doi:10.1007/978-3-642-36678-9_7)

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Abstract

Echocardiography plays an important part in diagnostic aid in cardiology. During an echocardiogram exam images or image sequences are usually taken from different locations with various directions in order to comprehend a comprehensive view of the anatomical structure of the 3D moving heart. The automatic classification of echocardiograms based on the viewpoint constitutes an essential step in a computer-aided diagnosis. The challenge remains the high noise to signal ratio of an echocardiography, leading to low resolution of echocardiograms. In this paper, a new synergy is proposed based on well-established algorithms to classify view positions of echocardiograms. Bags of Words (BoW) are coupled with linear SVMs. Sparse coding is employed to train an echocardiogram video dictionary based on a set of 3D SIFT descriptors of space-time interest points detected by a Cuboid detector. Multiple scales of max pooling features are applied to representat the echocardiogram video. The linear multiclass SVM is employed to classify echocardiogram videos into eight views. Based on the collection of 219 echocardiogram videos, the evaluation is carried out. The preliminary results exhibit 72% Average Accuracy Rate (AAR) for the classification with eight view angles and 90% with three primary view locations.

Item Type: Book Section
Research Areas: A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 10321
Useful Links:
Depositing User: Xiaohong Gao
Date Deposited: 01 Apr 2013 08:37
Last Modified: 13 Oct 2016 14:26
ISBN: 9783642366772
URI: https://eprints.mdx.ac.uk/id/eprint/10321

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