Dimitrios Markonis

HES-SO Valais-Wallis | HES-SO · Bereich eHealth

The Khresmoi project is developing a multilingual multimodal search and access system for medical and health information and documents. This scientific demonstration presents the current state of the Khresmoi integrated system, which includes components for text and image annotation, semantic search, search by image similarity and machine translati...

Objectives: The main objective of this study is to learn more on the image use and search requirements of radiologists. These requirements will then be taken into account to develop a new search system for images and associated meta data search in the Khresmoi project. Methods: Observations of the radiology workflow, case discussions and a liter...

Journal images represent an important part of the knowledge stored in the medical literature. Figure classification has received much attention as the information of the image types can be used in a variety of contexts to focus image search and filter out unwanted information or ”noise”, for example non–clinical images. A major problem in figure cl...

Image retrieval is a complex task that differs according to the context and the user requirements in any specific field, for example in a medical environment. Search by text is often not possible or optimal and retrieval by the visual content does not always succeed in modelling high-level concepts that a user is looking for. Modern image retrieval...

Medical image retrieval can assist physicians in finding information supporting their diagnosis and fulfilling information needs. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time available for information search is often short. Relevance feedback is a powerful tool...

This article reports the user-oriented evaluation of a text- and content-based medical image retrieval system. User tests with radiologists using a search system for images in the medical literature are presented. The goal of the tests is to assess the usability of the system, identify system and interface aspects that need improvement and useful a...

Searching for medical image content is a regular task for many physicians, especially in radiology. Retrieval of medical images from the scientific literature can benefit from automatic modality classification to focus the search and filter out non–relevant items. Training datasets are often unevenly distributed regarding the classes resulting some...

To help managing the large amount of biomedical images produced, image information retrieval tools have been developed to help accessing the right information at the right moment. To provide a test bed for image retrieval evaluation the ImageCLEFmed benchmark proposes a biomedical classification task that focuses on determining the image modality o...

Medical images are important to physicians for diagnosis and treatment planning. The number of images produced in medical institutions bas been increasing rapidly over the past years. Making these imaging data available and allowing medical professionals to perform retrieval based on visual characteristics of images is the challenge that content-ba...

Wearable computing devices are starting to change the way users interact with computers and the Internet. Among them, Google Glass includes a small screen located in front of the right eye, a camera filming in front of the user and a small computing unit. Google Glass has the advantage to provide online services while allowing the user to perform t...

Large amounts of medical images are being produced to help physicians in diagnosis and treatment planning. These images are then archived in PACS (Picture Archival and Communication Systems) and usually they are only reused in the context of the same patient during further visits. Medical image retrieval systems allow medical professionals to searc...

Retrieval systems can supply similar cases with a proven diagnosis to a new example case under observation to help clinicians during their work. The ImageCLEFmed evaluation campaign proposes a framework where research groups can compare case–based retrieval approaches. This paper focuses on the case–based task and adds results of the compound figur...

Medical image retrieval can assist physicians in finding information supporting their diagnosis. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evalua...

To help managing the large amount of biomedical images produced, image information retrieval tools have been developed to help accessing the right information at the right moment. To provide a test bed for image retrieval evaluation the ImageCLEFmed benchmark proposes a biomedical classification task that focuses on determining the image modality o...

Content-based image retrieval (CBIR) has often been proposed to assist medical decision making in complement to textual information search. However, applications of this novel technology have rarely reached the end users. The study presented in this paper describes the design and setup for performing pilot user tests in order to assess a medical in...

PURPOSE/AIM The enormous amount of visual data in PACS and the medical literature grows exponentially, also with the appearance of new imaging methods. Most current information search tools in radiology do not fully exploit new technologies and often allow only patient-based access. In this study, the medical image search prototype of the KHRESMOI...

BACKGROUND Research has shown that state-of-the-art, texture-analysis algorithms, applied for pathology detection in the medical domain, perform with varying degree. This variation makes the decision difficult which algorithm is best for a specific domain. During this talk we focus on content-based tissue-analysis algorithms for lung pathology dete...

The number of biomedical publications has increased noticeably in the last 30 years. Clinicians and medical researchers regularly have unmet information needs but require more time for searching than is usually available to find publications relevant to a clinical situation. The techniques described in this article are used to classify images from...

The growth of the amount of medical image data produced on a daily basis in modern hospitals forces the adaptation of traditional medical image analysis and indexing approaches towards scalable solutions. The number of images and their dimensionality increased dramatically during the past 20 years. We propose solutions for large-scale medical image...

This article presents the participation of the medGIFT group in ImageCLEFmed 2012. Since 2004, the group has participated in the medical image retrieval tasks of ImageCLEF each year. There are three types of tasks for ImageCLEFmed 2012: modality classification, image-based retrieval and case–based retrieval. The medGIFT group participated in all th...

Khresmoi is a European Integrated Project developing a multilingual multimodal search and access system for medical and health information and documents. It addresses the challenges of searching through huge amounts of medical data, including general medical information available on the internet, as well as radiology data in hospital archives. It i...

The biomedical literature published regularly has increased strongly in past years and keeping updated even in narrow domains is difficult. Images represent essential information of their articles and can help to quicker browse through large volumes of articles in connection with keyword search. Content–based image retrieval is helping the retrieval...

BACKGROUND Research has shown that efficient and quick access to comparable cases together with corresponding radiology reports increases radiological assessment quality. Typically search queries within a PACS are limited to meta data, such as those contained in DICOM images. This limits the effectiveness of searches, since they do not use the actu...

BACKGROUND Radiology is strongly connected with image use and search. However, the management and reuse of the overwhelming amount of medical image data produced by hospitals is not yet satisfactory. Search by using patient IDs or keywords does not allow exploiting the full richness of large databases of images with attached diagnoses due to scarce...

CONCLUSION Image search of radiologists can be improved by analyzing approaches for visual/textual retrieval based on clearly defined requirements. Observing the image use can help finding patterns for improving existing tools and adapt them to the end user needs. This study is part of a project on the radiologists’ image search needs. It adds aspe...

The purpose of this paper is to present a Remote Laboratory on embedded systems focused in real-time digital image processing. This laboratory consists of a Main Web Server and several Workstations which are designed for digital image retrieval from a CMOS Image Sensor and real-time image processing on a Digital Signal Processor development platfor...