Automated Textural Classification of Osteoarthritis Magnetic Resonance Images

Osteoarthritis (OA) is the most common cause of disability in the United Kingdom and United States. Identifying the rate of OA progression remains an important clinical and research challenge for early disease monitoring. Texture analysis of tibial subchondral bone using magnetic resonance imaging (MRI) has demonstrated the ability to discriminate between different stages of OA. This work combines texture analysis with machine learning methods (Lasso, Decision Tree, and Neural Network) to predict radiographic disease progression over 3 years, trained using data from the Osteoarthritis Initiative. We achieved high sensitivity (86%), specificity (64%) and accuracy (74%) for predictions of OA progression.The authors acknowledge research support from the National Institute of Health Research Cambridge Biomedical Research Centre. RT acknowledges the support of the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/L016516/1 for the University of Cambridge Centre for Doctoral Training, the Cambridge Centre for Analysis. JK and JM acknowledge support by GlaxoSmithKline. AM acknowledges research support from Arthritis Research UK; Tissue Engineering Centre award. FG acknowledges research support from Cancer Research UK

Content Based Image Retrieval using CMM+GWT and SVM Classifier

Content based Image Retrieval Process Depending on New Matching Strategy. In this paper Proposed Model composed of four Major Phases: feature extraction, Dimensionality Reduction, ANN Classifier and Matching Strategy. feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern(DBPSP). Dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. Matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class

Multilayer Complex Network Descriptors for Color-Texture Characterization

A new method based on complex networks is proposed for color-texture analysis. The proposal consists on modeling the image as a multilayer complex network where each color channel is a layer, and each pixel (in each color channel) is represented as a network vertex. The network dynamic evolution is accessed using a set of modeling parameters (radii and thresholds), and new characterization techniques are introduced to capt information regarding within and between color channel spatial interaction. An automatic and adaptive approach for threshold selection is also proposed. We conduct classification experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT. Results among various literature methods are compared, including deep convolutional neural networks with pre-trained architectures. The proposed method presented the highest overall performance over the 5 datasets, with 97.7 of mean accuracy against 97.0 achieved by the ResNet convolutional neural network with 50 layers.Comment: 20 pages, 7 figures and 4 table

Survey of Error Concealment techniques: Research directions and open issues

© 2015 IEEE. Error Concealment (EC) techniques use either spatial, temporal or a combination of both types of information to recover the data lost in transmitted video. In this paper, existing EC techniques are reviewed, which are divided into three categories, namely Intra-frame EC, Inter-frame EC, and Hybrid EC techniques. We first focus on the EC techniques developed for the H.264/AVC standard. The advantages and disadvantages of these EC techniques are summarized with respect to the features in H.264. Then, the EC algorithms are also analyzed. These EC algorithms have been recently adopted in the newly introduced H.265/HEVC standard. A performance comparison between the classic EC techniques developed for H.264 and H.265 is performed in terms of the average PSNR. Lastly, open issues in the EC domain are addressed for future research consideration