A global description of medical imaging with high precision

Abstract

This paper explores our solution aiming to provide efficient retrieval of medical imaging. Depending on the user, the same image can be described through different views. In essence, an image can be described on the basis of either low-level properties, such as texture or color; contextual data, such as date of acquisition or author; or semantic content, such as real-world objects and relations. Our approach consists of providing a multispaced description model capable of integrating different facets (or views) of the medical image. Visual retrieval solutions are recommended and are the most appropriated for noncomputer-science users. However, current visual languages suffer from several problems, especially ambiguities generated by the user and/or the system, and imprecision at different levels of image description. In this paper, we expose our solution and demonstrate how spatial precision of medical image content and ambiguities can be resolved. An implementation called Medical Image Management System (MIMS) has been realized to prove our proposition. A set of tests has been deployed to validate our prototype.

Image Retrieval and Classification of Carotid Plaque Ultrasound Images

“…The consultation of medical CBIR systems in the frame of a computeraided diagnosis aim not to replace the physician but to assist him in making a diagnosis by providing similar images from similar cases and helping him to consider other options and treatments used in other similar cases and not anticipated by the first examination. In previous work in image retrieval systems [2][3][4][5][6][7] several new features were proposed for different applications, which in some cases were problem dependent [4], or as in [3] a medical image management system was developed which was more based on semantics rather than image content for retrieval. Also in some of the previous studies, partly similar features to the ones implemented in this work were used, like histograms [5] or correlograms [7], or similar classifiers like SOM in [6] but to the best of our knowledge no other image retrieval system has been implemented for the retrieval and classification of carotid plaques.…”

“…There were several approaches in dealing with image retrieval systems. Chbeir and Favetta in their work [3] developed a medical image management system, which translated contextual and *Address correspondence to this author at the Department of Computer Science, University, of Cyprus, 75 Kallipoleos Str, P.O. Box 20537, 1678 Nicosia, Cyprus; Tel: +35799543747; E-mail: cschr2@ucy.ac.cy semantic data of medical imaging into SQL queries in order to accomplish efficient image retrieval.…”

Foto2Events: From Photos to Event Discovery and Linking in Online Social Networks

“…DIRECT: is a set of directional relations describing the order between e i and e j according to a direction. In the literature [35], 14 directional relations are considered (e.g., north, south, east, west, left, right, down, etc.). topologic: is an exclusive topological relation that describes the intersection and the incidence between events.…”

Content-Based Medical Image Retrieval

“…As such, the development of global features that can represent an entire medical image database seems to be practically infeasible. Aiming to provide eYcient retrieval of generic medical images with coherent and eVective objectivity of interpretation at diVerent facets (or views) of medical images, Chbeir and Favetta [195] proposed a global description of medical images in which a hyperspaced image data model was constructed [175,196]. The data model was structured as a multispaced form in which each space contained a set of features (contextual, physical, spatial, and semantic), and considered the medical image as a composition of contextual and content feature spaces.…”