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Proceedings ArticleDOI

Echocardiogram view classification with appearance and spatial distributions

TL;DR: 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.
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Journal ArticleDOI
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

46,906 citations

01 Jan 2011
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Abstract: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.

14,708 citations


"Echocardiogram view classification ..." refers methods in this paper

  • ...In our approach, local affine-invariant SIFT features [7] are used to generate classification model for PLAX and SAX ultrasound images....

    [...]

Proceedings ArticleDOI
17 Jun 2006
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Abstract: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s "gist" and Lowe’s SIFT descriptors.

8,736 citations


"Echocardiogram view classification ..." refers background or methods in this paper

  • ...In past, methods that capture statistical distributions of the discriminative features and generalize the across variations in a given class, had shown significant performance in visual categorization [5] [6]....

    [...]

  • ...On the other hand, the Spatial Pyramid Histogram of Words (SPHOW) model captures the spatial arrangement of features and considers their absolute location in image[6]....

    [...]

Proceedings Article
01 Jan 2004
TL;DR: This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches and shows that it is simple, computationally efficient and intrinsically invariant.
Abstract: We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches. We propose and compare two alternative implementations using different classifiers: Naive Bayes and SVM. The main advantages of the method are that it is simple, computationally efficient and intrinsically invariant. We present results for simultaneously classifying seven semantic visual categories. These results clearly demonstrate that the method is robust to background clutter and produces good categorization accuracy even without exploiting geometric information.

5,046 citations


"Echocardiogram view classification ..." refers methods in this paper

  • ...In past, methods that capture statistical distributions of the discriminative features and generalize the across variations in a given class, had shown significant performance in visual categorization [5] [6]....

    [...]

  • ...One of the widely used categorization model is the bag of visual words [5]....

    [...]

Proceedings ArticleDOI
Jing Huang1, S.R. Kumar1, Mandar Mitra1, Wei-Jing Zhu1, Ramin Zabih1 
17 Jun 1997
TL;DR: Experimental evidence suggests that this new image feature called the color correlogram outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.
Abstract: We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogram robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.

1,956 citations


"Echocardiogram view classification ..." refers background in this paper

  • ...al [8] proposed the color correlogram for image indexing....

    [...]