The application of KAZE features to the classification echocardiogram videos

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

Task-Dependent Inhomogeneous Muscle Activities within the Bi-Articular Human Rectus Femoris Muscle

The motor nerve of the bi-articular rectus femoris muscle is generally split from the femoral nerve trunk into two sub-branches just before it reaches the distal and proximal regions of the muscle. In this study, we examined whether the regional difference in muscle activities exists within the human rectus femoris muscle during maximal voluntary isometric contractions of knee extension and hip flexion. Surface electromyographic signals were recorded from the distal, middle, and proximal regions. In addition, twitch responses were evoked by stimulating the femoral nerve with supramaximal intensity. The root mean square value of electromyographic amplitude during each voluntary task was normalized to the maximal compound muscle action potential amplitude (M-wave) for each region. The electromyographic amplitudes were significantly smaller during hip flexion than during knee extension task for all regions. There was no significant difference in the normalized electromyographic amplitude during knee extension among regions within the rectus femoris muscle, whereas those were significantly smaller in the distal than in the middle and proximal regions during hip flexion task. These results indicate that the bi-articular rectus femoris muscle is differentially controlled along the longitudinal direction and that in particular the distal region of the muscle cannot be fully activated during hip flexion

Learning color receptive fields and color differential structure

\u3cp\u3eIn this paper we study the role of brain plasticity, and investigate the emergence and self-emergence of receptive fields from scalar and color natural images by principal component analysis of image patches. We describe the classical experiment on localized PCA on center-surround weighted patches of natural scalar images. The resulting set turns out to show great similarity to Gaussian spatial derivatives, and exhibits steerability behavior. We then relate the famous experiment by Blakemore of training a cat with only visual horizontal bar information with PCA analysis of images with primarily unidirectional structure. PCA is performed for patches of RGB natural color images. The resulting profiles resemble spatio-spectral operators extracting color differential structure and shape. We discuss how spatio-spectral Gaussian derivative operators along the wavelength dimension can be modeled, originally proposed by Koenderink, and based on Hering's opponent color theory. The discussion puts the PCA findings in the perspective of multi-scale Gaussian differential geometry, multi-orientation sub-Riemannian geometry, and PCA on affinity matrices for contextual models.\u3c/p\u3

The differential structure of images

The chapter focuses on modern computer vision algorithms, used to extract robust multi-scale differential features from discrete images, like corners, T-junctions, edges. The algorithms have an axiomatic basis, and are inspired by modern insights in possible functional circuits in the visual system in the human brain. Applications focus on computer-aided diagnosis and industrial vision tasks

Image Processing on Diagnostic Workstations

Medical workstations have developed into the super-assistants of radiologists. The overwhelming production of images, hardware that rapidly became cheaper and powerful 3D visualization and quantitative analysis software have all pushed the developments from simple PACS viewing into a really versatile viewing environment. This chapter gives an overview of these developments, aimed at radiologists’ readership. Many references and internet are given which discuss the topics in more depth than is possible in this short paper. This paper is necessarily incomplete