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

Trace elements in hemodialysis patients: a systematic review and meta-analysis

<p>Abstract</p> <p>Background</p> <p>Hemodialysis patients are at risk for deficiency of essential trace elements and excess of toxic trace elements, both of which can affect health. We conducted a systematic review to summarize existing literature on trace element status in hemodialysis patients.</p> <p>Methods</p> <p>All studies which reported relevant data for chronic hemodialysis patients and a healthy control population were eligible, regardless of language or publication status. We included studies which measured at least one of the following elements in whole blood, serum, or plasma: antimony, arsenic, boron, cadmium, chromium, cobalt, copper, fluorine, iodine, lead, manganese, mercury, molybdenum, nickel, selenium, tellurium, thallium, vanadium, and zinc. We calculated differences between hemodialysis patients and controls using the differences in mean trace element level, divided by the pooled standard deviation.</p> <p>Results</p> <p>We identified 128 eligible studies. Available data suggested that levels of cadmium, chromium, copper, lead, and vanadium were higher and that levels of selenium, zinc and manganese were lower in hemodialysis patients, compared with controls. Pooled standard mean differences exceeded 0.8 standard deviation units (a large difference) higher than controls for cadmium, chromium, vanadium, and lower than controls for selenium, zinc, and manganese. No studies reported data on antimony, iodine, tellurium, and thallium concentrations.</p> <p>Conclusion</p> <p>Average blood levels of biologically important trace elements were substantially different in hemodialysis patients, compared with healthy controls. Since both deficiency and excess of trace elements are potentially harmful yet amenable to therapy, the hypothesis that trace element status influences the risk of adverse clinical outcomes is worthy of investigation.</p

Enhanced imaging colonoscopy facilitates dense motion-based 3D reconstruction

We propose a novel approach for estimating a dense 3D model of neoplasia in colonoscopy using enhanced imaging endoscopy modalities. Estimating a dense 3D model of neoplasia is important to make 3D measurements and to classify the superficial lesions in standard frameworks such as the Paris classification. However, it is challenging to obtain decent dense 3D models using computer vision techniques such as Structure-from-Motion due to the lack of texture in conventional (white light) colonoscopy. Therefore, we propose to use enhanced imaging endoscopy modalities such as Narrow Band Imaging and chromoendoscopy to facilitate the 3D reconstruction process. Thanks to the use of these enhanced endoscopy techniques, visualization is improved, resulting in more reliable feature tracks and 3D reconstruction results. We first build a sparse 3D model of neoplasia using Structure-from-Motion from enhanced endoscopy imagery. Then, the sparse reconstruction is densified using a Multi-View Stereo approach, and finally the dense 3D point cloud is transformed into a mesh by means of Poisson surface reconstruction. The obtained dense 3D models facilitate classification of neoplasia in the Paris classification, in which the 3D size and the shape of the neoplasia play a major role in the diagnosis

Using Local, Contextual, and Deep Convolutional Neural Network Features in Image Registration

Image registration is a well-known problem that arises in many applications in the fields of computer vision, remote sensing, and medical imaging. Many registration methods have been proposed in the literature. However, no single method works well in all kinds of images. In this work, local features and context-based augmented features are used in order to improve the accuracy of the image registration. Furthermore, an attempt has been made to use deep convolutional neural network features on top of those features for further improvement. The paper presents comparative results on image registration with and without feature augmentation and the deep convolutional neural network features. The results from the methods on a widely used benchmark dataset from the University of Oxford confirm improvement in the accuracy of image registration when local and augmented features are used