This work was part of SUNY at Buffalo’s overall participation in cross-language retrieval of image collections (ImageCLEF). Our main goal was to explore the combination of Content-Based Image Retrieval (CBIR) and text retrieval of medical images that have clinical annotations in English, French and German. We used a system that combined the content-based image retrieval system GIFT and the well...

JCHPS Special Issue 1: February 2017 www.jchps.com Page 212 A Medical Image Retrieval System using Gabor Barcode B. Nandhini*, S. Gowri Department of ECE, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India. *Corresponding author: E-Mail: [email protected] ABSTRACT Advances in digital imaging technologies and the increasing prevalence of picture archival systems have led to an...

Medical image retrieval to search for clinically relevant and visually similar images depicting suspecious lesions have been attracting research interest. Content-based image retrieval (CBIR) is an important alternate and complement to traditional text-based retrieval using keywords. We have implemented CBIR system based on effective use of texture information within the images obtained by stat...

In this article, we describe the used technologies and experimental results for the medical retrieval task at ImageCLEF 2005. The topics of competition this year contain both semantic queries and visual queries. The content-based approach containing four image features and the text-based approach using word expansion are developed to accomplish the mission. The experimental results show that th...

Content-based image retrieval is starting to become an increasingly important factor in medical imaging research and image management systems. Several retrieval systems and methodologies exist and are used in a large variety of applications from automatic labelling of images to diagnostic aid and image classification. Still, it is very hard to compare the performance of these systems as the use...

Image retrieval is an important research field which has received great attention in the last decades. In this paper, we present an approach for the image retrieval based on the combination of text-based and content-based features. For text-based features, keywords and for content-based features, color and texture features have been used. Query in this system contains some keywords and an input...

We propose a novel image retrieval framework centered around classi cation-driven search for a weighted similarity metric for image retrieval. This approach is rmly rooted in Bayes decision theory. Given a well-de ned image set, we argue that image classi cation and image retrieval share fundamentally the same goal. Thus, the distance metric de ning a classi er that performs well on the image s...

Among the numerous 3D medical image segmentation methods proposed in the literature, very few have intended to provide topologically satisfying results, a fortiori for multiple object segmentation. In this paper, we present a method devoted to parallel segmentation of the main classes of cerebral tissues from 3D magnetic resonance imaging data. This method is based on a multi-class discrete def...