Mid-level Image Representations For Real-time Heart View Plane Classification Of Echocardiograms.

In this paper, we explore mid-level image representations for real-time heart view plane classification of 2D echocardiogram ultrasound images. The proposed representations rely on bags of visual words, successfully used by the computer vision community in visual recognition problems. An important element of the proposed representations is the image sampling with large regions, drastically reducing the execution time of the image characterization procedure. Throughout an extensive set of experiments, we evaluate the proposed approach against different image descriptors for classifying four heart view planes. The results show that our approach is effective and efficient for the target problem, making it suitable for use in real-time setups. The proposed representations are also robust to different image transformations, e.g., downsampling, noise filtering, and different machine learning classifiers, keeping classification accuracy above 90%. Feature extraction can be performed in 30 fps or 60 fps in some cases. This paper also includes an in-depth review of the literature in the area of automatic echocardiogram view classification giving the reader a through comprehension of this field of study.6666-8

Nearest Neighbors Distance Ratio Open-Set Classifier

<p>Feature vectors used in "Nearest neighbors distance ratio open-set classifier" paper to appear in Springer Machine Learning journal.</p> <p>In the <strong>15-Scenes</strong> (15scenes.dat) dataset, with 15 classes, the 4,485 images were represented by a bag-of-visual-word vector created with soft assignment and max pooling, based on a codebook of 1,000 Scale Invariant Feature Transform (SIFT) codewords.</p> <p>The 26 classes of the <strong>letter</strong> (letter.dat) dataset represent the letters of the English alphabet (black-and-white rectangular pixel displays). The 20,000 samples contain 16 attributes.</p> <p>The <strong>Auslan</strong> (auslan.dat) dataset contains 95 classes of Australian Sign Language (Auslan) signs collected from a volunteer native Auslan signer. Data was acquired using two Fifth Dimension Technologies (5DT) gloves hardware and two Ascension Flock-of-Birds magnetic position trackers. There are 146,949 samples represented with 22 features (<em>x</em>, <em>y</em>, <em>z</em> positions, bend measures, etc).</p> <p>The <strong>Caltech-256</strong> (caltech256.dat) dataset comprises 256 object classes. The feature vectors consider a bag-of-visual-words characterization approach and contain 1,000 features, acquired with dense sampling, SIFT descriptor for the points of interest, hard assignment, and average pooling. In total, there are 29,780 samples.</p> <p>The <strong>ALOI</strong> (aloi.dat) dataset has 1,000 classes and 108 samples for each class (108,000 in total). The features were extracted with the Border/Interior (BIC) descriptor and contain 128 dimensions.</p> <p>The <strong>ukbench</strong> (ukbench.dat) dataset comprises 2,550 classes of four images each. In our work, the images were represented with BIC descriptor (128 dimensions).</p