In this paper, we use Support Vector Machine (SVM) to learn image feature characteristics for assisting the task of image classification. The ImageCLEF 2005 evaluation offers a superior test bed for medical image content retrieval. Several image visual features (including histogram, spatial layout, coherence moment and gabor features) have been employed in this paper to categorize the 1,000 tes...

Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representi...

This paper represents the first participation of the Institute of Statistical Studies and Research at Cairo University group in CLEF 2009-Medical image retrieval track. Our system uses Lemur toolkit for text retrieval. The main objective is to carry out retrieving medical image depending on associated image text. We experimented with different text features such as article title, image caption ...

This research work is to develop an efficient and powerful medical search engine to classify and search the radiographic medical images. It focuses on bag of visual words image representation and a similarity matching technique to represent match and retrieve the similar images. This work addresses the issues in content based image retrieval for medical images. In this system can handles differ...

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 medical field, digital images are produced in ever increasing quantities and used for diagnostics and therapy. The swift expansion of digital medical images has enforced the requirement of efficient Content-based image retrieval system for retrieving medical images that are visually similar to query image. Such systems provide great assistance to doctors in clinical care and research. In thi...

This paper describes the participation of Tel Aviv University Medical Image Processing Laboratory group at the ImageClef 2008 medical retrieval and medical annotation tasks. In both tasks we have used the bag-of-words approach for image representation. We submitted two purely visual automatic runs to the medical retrieval task, which used different normalization in the feature extraction stage....

image classification is an issue which utilizes image processing, pattern recognition and classification methods. automatic medical image classification is a progressive area in image classification and it expected to be more developed in the future. due to this fact that automatic diagnosis which use intelligent methods such as medical image classification can assist pathologists by providing ...

This article describes the participation of the Communications Engineering Branch (CEB), a division of the Lister Hill National Center for Biomedical Communications, in the ImageCLEF 2011 medical retrieval track. Our methods encompass a variety of techniques relating to textand content-based image retrieval. Our textual approaches primarily utilize the Unified Medical Language System (UMLS) syn...

This is the second participation of Institute of Statistical Studies and Research (ISSR) group in CLEF 2010-Medical image retrieval track. This paper describes our experiments in monolingual and multilingual tasks. First, we test Paragraph Extraction (PE) and Sentence Selection (SS) approaches on the classical medical retrieval task (Ad-hoc), as well as on Case-based retrieval. Second, we compa...