The application of KAZE features to the classification echocardiogram videos
Conference paper
Li, W., Qian, Y., Loomes, M. and Gao, X. 2015. The application of KAZE features to the classification echocardiogram videos. First International Workshop Multimodal Retrieval in the Medical Domain (MRMD 2015). Vienna, Austria 29 Mar 2015 Springer. pp. 61-72 https://doi.org/10.1007/978-3-319-24471-6_6
Type | Conference paper |
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Title | The application of KAZE features to the classification echocardiogram videos |
Authors | Li, W., Qian, Y., Loomes, M. and Gao, X. |
Abstract | In the computer vision field, both approaches of SIFT and SURF are prevalent in the extraction of scale-invariant points and have demonstrated a number of advantages. However, when they are applied to medical images with relevant low contrast between target structures and surrounding regions, these approaches lack the ability to distinguish salient features. Therefore, this research proposes a different approach by extracting feature points using the emerging method of KAZE. As such, to categorise a collection of video images of echocardiograms, KAZE feature points, coupled with three popular representation methods, are addressed in this paper, which includes the bag of words (BOW), sparse coding, and Fisher vector (FV). In comparison with the SIFT features represented using Sparse coding approach that gives 72% overall performance on the classification of eight viewpoints, KAZE feature integrated with either BOW, sparse coding or FV improves the performance significantly with the accuracy being 81.09%, 78.85% and 80.8% respectively. When it comes to distinguish only three primary view locations, 97.44% accuracy can be achieved when employing the approach of KAZE whereas 90% accuracy is realised while applying SIFT features. |
Keywords | Classification of echocardiogram videos; KAZE ; 3D SIFT; SURF ; Sparse coding; SVM; Bag of words; Fisher vector |
Conference | First International Workshop Multimodal Retrieval in the Medical Domain (MRMD 2015) |
Page range | 61-72 |
Proceedings Title | Multimodal Retrieval in the Medical Domain: First International Workshop, MRMD 2015, Vienna, Austria, March 29, 2015, Revised Selected Papers |
Series | Lecture Notes in Computer Science |
ISSN | 0302-9743 |
Electronic | 1611-3349 |
ISBN | |
Hardcover | 9783319244709 |
Publisher | Springer |
Publication dates | |
Dec 2015 | |
Publication process dates | |
Deposited | 27 Apr 2015 |
Accepted | 15 Feb 2015 |
Output status | Published |
Copyright Statement | The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24471-6_6 |
Additional information | Published as a chapter in: Multimodal Retrieval in the Medical Domain, Volume 9059 of the series Lecture Notes in Computer Science pp 61-72 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-24471-6_6 |
Scopus EID | 2-s2.0-84952766202 |
Web of Science identifier | WOS:000375149600006 |
Language | English |
File |
https://repository.mdx.ac.uk/item/85209
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