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

Conference item

Qian, Y., Wang, L., Wang, C. and Gao, X. 2013. The synergy of 3D SIFT and sparse codes for classification of viewpoints from echocardiogram videos. Greenspan, H., Müller, H. and Syeda-Mahmood, T. (ed.) 3rd MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support. Nice, France 01 - 01 Oct 2012 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-642-36678-9_7
TitleThe synergy of 3D SIFT and sparse codes for classification of viewpoints from echocardiogram videos
AuthorsQian, Y., Wang, L., Wang, C. and Gao, X.
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.

Keywords3D SIFT; Classification of Echocardiogram Video; Cuboid Detector; Sparse Coding; SVM
Research GroupArtificial Intelligence group
Conference3rd MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support
Proceedings Title Medical Content-Based Retrieval for Clinical Decision Support Third MICCAI International Workshop, MCBR-CDS 2012, Nice, France, October 1st, 2012, Revised Selected Papers
SeriesLecture Notes in Computer Science
EditorsGreenspan, H., Müller, H. and Syeda-Mahmood, T.
ISSN0302-9743
Electronic1611-3349
ISBN
Paperback9783642366772
Electronic9783642366789
PublisherSpringer
Place of publicationBerlin, Heidelberg
Publication process dates
Deposited01 Apr 2013
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-642-36678-9_7
Scopus EID2-s2.0-84874424795
LanguageEnglish
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Gao, X., Hong, K., Passmore, P., Podladchikova, L. and Shaposhnikov, D. 2008. Colour vision model-based approach for segmentation of traffic signs. EURASIP Journal on Image and Video Processing. 2008. https://doi.org/10.1155/2008/386705
Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach
Anishenko, S., Osimov, V., Shaposhnikov, D., Podladchikova, L., Comley, R. and Gao, X. 2008. Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach. Puuronen, S., Pechenizkiy, M., Tsymbal, A. and Lee, D. (ed.) 21st IEEE International Symposium on Computer-Based Medical Systems. Jyvaskyla, Finland 17 - 19 Jun 2008 IEEE Computer Society. pp. 50-52 https://doi.org/10.1109/CBMS.2008.19
Detection of head motions using a vision model
Gao, X., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J. 2007. Detection of head motions using a vision model. Bashshur, R. (ed.) 3rd IASTED International Conference on Telehealth. Montreal, QC, Canada 31 May - 01 Jun 2007 Anaheim, CA Acta Press. pp. 167-171
Recognition of traffic signs based on their colour and shape features extracted using human vision models
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K. and Shevtsova, N. 2006. Recognition of traffic signs based on their colour and shape features extracted using human vision models. Journal of Visual Communication and Image Representation. 17 (4), pp. 675-685. https://doi.org/10.1016/j.jvcir.2005.10.003
Colour management in telemedicine
Gao, X. 2004. Colour management in telemedicine. Hamza, M. (ed.) 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 Anaheim, CA Acta Press. pp. 361-364
Application of vision models to traffic sign recognition
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2004. Application of vision models to traffic sign recognition. Kaynak, O., Alpaydin, E., Oja, E. and Xu, L. (ed.) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Istanbul, Turkey 26 - 29 Sep 2003 Berlin, Heidelberg Springer. https://doi.org/10.1007/3-540-44989-2_131
Towards content-based retrieval for wallpaper images
Qian, Y., Tully, C., Hendon, Z. and Gao, X. 2003. Towards content-based retrieval for wallpaper images. 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 pp. 305-309
Vision models based identification of traffic signs
Gao, X., Podladchikova, L., Shaposhnikov, D., Shevtsova, N., Hong, K., Batty, S., Golovan, A. and Gusakova, V. 2002. Vision models based identification of traffic signs. 1st European Conference on Colour Graphics, Imaging, and Vision. University of Poitiers, France 02 - 05 Apr 2002 Society for Imaging Science and Technology.
Content based retrieval of lesioned brain images
Batty, S., Blandford, A., Clark, J., Fryer, T. and Gao, X. 2002. Content based retrieval of lesioned brain images. Siegel, E. and Huang, H. (ed.) SPIE Medical Imaging 2002. San Diego, California, United States 23 - 28 Feb 2002 Bellingham Society of Photo-optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.466997
A method of vessel tracking for vessel diameter measurement on retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2001. A method of vessel tracking for vessel diameter measurement on retinal images. 2001 International Conference on Image Processing. Thessaloniki, Greece 07 - 10 Oct 2001 IEEE.
Computer algorithms for the automated measurement of retinal arteriolar diameters
Chapman, N., Witt, N., Gao, X., Bharath, A., Stanton, A., Thom, S. and Hughes, A. 2001. Computer algorithms for the automated measurement of retinal arteriolar diameters. British Journal of Ophthalmology. 85 (1), pp. 74-79. https://doi.org/10.1136/bjo.85.1.74
Quantification and characterization of arteries in retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2000. Quantification and characterization of arteries in retinal images. Computer Methods and Programs in Biomedicine. 63 (2), pp. 133-146. https://doi.org/10.1016/S0169-2607(00)00082-1