Proceedings ArticleDOI
Echocardiogram view classification with appearance and spatial distributions
Ronak Gupta,Santanu Chaudhury,Navneeth Subramanian,Satish Govind +3 more
- pp 655-658
TLDR
An approach for view classification, Spatial Pyramid Histogram of Words which successfully models the appearance and shape distributions of object class and shows a classification accuracy of 98.3% on an exhaustive database of 703 ultrasound images.Abstract:
When imaging the heart, using a 2D ultrasound probe, different views can manifest depending on the location and angulations of the probe. Some of these views have been labeled as standard views, due to the presentation and ease of assessment of key cardiac structures in them. We present an approach for automatic recognition and classification of these standard views, as a potential enabler for automated measurements or detection of noise — all without a human in the loop. We present an approach for view classification, Spatial Pyramid Histogram of Words which successfully models the appearance and shape distributions of object class. We demonstrate the effectiveness of this technique for the task of discrimination between the B-mode Parasternal Long Axis (PLAX) and the Short Axis (SAX) echocardiograms. For this task, our method shows a classification accuracy of 98.3% on an exhaustive database of 703 ultrasound images.read more
References
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Proceedings ArticleDOI
Automatic Cardiac View Classification of Echocardiogram
TL;DR: A fully automatic system for cardiac view classification of echocardiogram is proposed based on a machine learning approach that extracts knowledge from an annotated database employing a multi-class Logit-boost algorithm.
Proceedings ArticleDOI
Image-Based Multiclass Boosting and Echocardiographic View Classification
Shaohua Kevin Zhou,Jin-hyeong Park,Bogdan Georgescu,Dorin Comaniciu,Constantine Simopoulos,Joanne Otsuki +5 more
TL;DR: This work tackles the problem of automatically classifying cardiac view for an echocardiographic sequence as a multiclass object detection using the LogitBoosting algorithm and proposes to learn a tree structure that focuses on the remaining classes to improve learning efficiency.
Proceedings ArticleDOI
Echocardiogram view classification using edge filtered scale-invariant motion features
TL;DR: A system for automatic view classification that exploits cues from both cardiac structure and motion in echocardiogram videos that consistently outperforms state-of-the-art methods in the popular four-view classification test.
Proceedings ArticleDOI
Automatic view classification of echocardiograms using Histogram of Oriented Gradients
TL;DR: An approach for automatic recognition and classification of these standard views - namely the Parasternal Long Axis (PLAX) and the Short Axis (SAX) B-mode echocardiograms and the Histogram of Oriented Gradients used as the discriminating feature.