Identification of Gastroenteric Viruses by Electron Microscopy Using Higher Order Spectral Features

Background: Many paediatric illnesses are caused by viral agents, for example, acute gastroenteritis. Electron microscopy can provide images of viral particles and can be used to identify the agents. Objectives: The use of electron microscopy as a diagnostic tool is limited by the need for high level of expertise in interpreting these images and the time required. A semi-automated method is proposed in this paper. Study design: The method is based on bispectal features that capture contour and texture information while providing robustness to shift, rotation, changes in size and noise. The magnification or true size of the viral particles need not be known precisely, but if available can be used additionally for improved classification. Viral particles from one or more images are segmented and analyzed to verify whether they belong to a particular class (such as Adenovirus, Rotavirus, etc.) or not. Two experiments were conducted—depending on the populations from which virus particle images were collected for training and testing, respectively. In the first, disjoint subsets from a pooled population of virus particles obtained from several images were used. In the second, separate populations from separate images were used. The performance of the method on viruses of similar size was separately evaluated using Astrovirus, HAV and Poliovirus. A Gaussian Mixture Model was used for the probability density of the features. A threshold on the log-likelihood is varied to study false alarm and false rejection trade-off. Features from many particles and/or likelihoods from independent tests are averaged to yield better performance. Results: An equal error rate (EER) of 2% is obtained for verification of Rotavirus (tested against three other viruses) when features from 15 viral particle images are averaged. It drops further to less than 0.2% when scores from two tests are averaged to make a decision. For verification of Astrovirus (tested against two others of the same size) the EER was less than 2% when 20 particles and two tests were used. Conclusion: Bispectral features and Gaussian mixture modelling of their probability density are shown to be effective in identifying viruses from electron microscope images. With the use of digital imaging in electron microscopes, this method can be fully automated

The aceToolbox: low-level audiovisual feature extraction for retrieval and classification

In this paper we present an overview of a software platform that has been developed within the aceMedia project, termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM), with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images

Portable Camera Based Assistive Pattern Recognition for Visually Challenged Persons

Choosing clothes, food recognition and traffic signal analysis are major challenges for visually impaired persons. The existing automatic clothing pattern recognition is also a challenging research problem due to rotation, scaling, illumination, and especially large intra class pattern variations. This project, a camera based assistive framework is proposed to help blind persons for identification of food pattern, clothe pattern and colors in their daily lives. The existing traffic signal using sensors method is difficult to analysis and many components used. A camera based traffic signal analysis method easy to handle, to provide clear traffic signal analysis and reduce the time delay. The system contains the following major components 1) a camera for capturing clothe, food and traffic signal images, a microphone for speech command input; 2) data capture and analysis to perform command control, recognize clothe patterns, food patterns and traffic signal identification by using a wearable computer and 3) a speaker to provide the name of audio outputs of clothe patterns and colors, food patterns and traffic signal analysis, as well as system status. To handle the large intra class variations, a novel descriptor, Radon Signature is proposed to capture the global directionality of clothe patterns, food patterns and traffic signal analysis. To evaluate the effectiveness of the proposed approach CCNY clothes Pattern dataset is used. Our approach achieves 92.55% recognition to improve the life quality, do not depend others. DOI: 10.17762/ijritcc2321-8169.15032