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The volume of academic paper submissions and publications is growing at an ever increasing rate. While this flood of research promises progress in various fields, the sheer volume of output inherently increases the amount of noise. We present a system to automatically separate papers with a high from those with a low likelihood of gaining citations as a means to quickly find high impact, high quality...
Computational visual atention models aims to emulate the Human Visual System performance in selecting relevant features for efficient visual scene processing. As a result, visual saliency maps highlights relevant visual patterns in an image, possibly associated with objects or specific concepts. In the analysis of medical images, this allows the radiologist or clinical expert to focus the attention...
Image modality classification categorizes images according to their type. It is an important module in the Open-iSM multimodal (text+image) search engine that retrieves figures from biomedical articles. It is a hierarchical classification where on the top level the input figures are classified into two general categories: regular images (X-ray, CT, MRI, photographs, etc.) vs. illustration images (cartoon...
In medical information retrieval research, automatically classifying X-ray images based on body-parts is a challenging problem. In ImageCLEF's 2015 campaign there was a contest where the participants were challenged to cluster X-ray images into different groups based on presence of particular body-part in that X-ray image. In brief the challenge was to classify given X-ray images primarily into five...
In this paper, we present and evaluate an interactive gesture controlled application using the Leap Motion for medical visualization, focusing on user satisfaction as an important component in the composition of the application success factors. Usability testings were conducted to verify important application requirements, among which, the asepsis in the working environment, accuracy of the interaction...
Bone texture characterization is important for differentiating osteoporotic and healthy subjects. Automated classification is however very challenging due to the high degree of visual similarity between the two types of images. In this paper, we propose to describe the bone textures by extracting dense sets of local descriptors and encoding them with the improved Fisher vector (IFV). Compared to the...
In this work, we present a gesture control system for aiding surgical procedures using the Kinect device. Several abstractions have been implemented, making this process simpler, at an affordable cost. Procedures for visualization and controlling of medical images, routinely performed in surgical theatres, were also successfully modeled, including gesture control of radiological images. We have also...
Worldwide, it is believed that there are between 1000 to 2000 skin conditions of which 20% are difficult to diagnose. An intelligent computer-aided diagnosing system not only helps patients with no or little access to health services but also can benefit typical general practitioners, who have received minimal dermatology training. We have built a challenging dataset containing 2309 images from 44...
Modality is a key facet in medical image retrieval, as a user is likely interested in only one of e.g. radiology images, flowcharts, and pathology photos. While assessing image modality is trivial for humans, reliable automatic methods are required to deal with large un-annotated image bases, such as figures taken from the millions of scientific publications. We present a multi-disciplinary approach...
Expert physicians are able to attain good Alzheimer's Disease (AD) diagnostic accuracy, relying on visual inspection of Positron Emission Tomography (PET) images only. Nevertheless, computerized methods have been implemented with similar or even better performance. We investigate the potential of the physician's experienced visual inspection to guide feature selection, in an automatic classification...
Medical databases have been a popular application field for image retrieval techniques during the last decade. More recently, much attention has been paid to the prediction of medical image modality (X-rays, MRI…) and the integration of the predicted modality into image retrieval systems. This paper addresses these two issues. On the one hand, we believe it is possible to design specific visual descriptors...
A correlation-enhanced similarity matching framework for medical image retrieval is presented in a local concept-based feature space. In this framework, images are presented by vectors of concepts that comprise of local color and texture patches of image regions in a multi-dimensional feature space. To generate the concept vocabularies and represent the images, statistical models are built using a...
This paper presents a medical image retrieval framework that uses visual concepts in a feature space employing statistical models built using a probabilistic multi-class support vector machine (SVM). The images are represented using concepts that comprise color and texture patches from local image regions in a multi-dimensional feature space. A major limitation of concept feature representation is...
In this paper, we investigate the influence of the clinical context of high-resolution computed tomography (HRCT) images of the chest on tissue classification. Evaluation of the classification performance is based on high-quality visual data extracted from clinical routine. The clinical attributes with highest information gain ratio show to be relevant and consistent for the classification of lung...
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