ABSTRACT
The impact of image pattern recognition on accessing large databases of medical images has recently been explored, and content-based image retrieval (CBIR) in medical applications (IRMA) is researched. At the present, however, the impact of image retrieval on diagnosis is limited, and practical applications are scarce. One reason is the lack of suitable mechanisms for query refinement, in particular, the ability to (1) restore previous session states, (2) combine individual queries by Boolean operators, and (3) provide continuous-valued query refinement. This paper presents a powerful user interface for CBIR that provides all three mechanisms for extended query refinement. The various mechanisms of man-machine interaction during a retrieval session are grouped into four classes: (1) output modules, (2) parameter modules, (3) transaction modules, and (4) process modules, all of which are controlled by a detailed query logging. The query logging is linked to a relational database. Nested loops for interaction provide a maximum of flexibility within a minimum of complexity, as the entire data flow is still controlled within a single Web page. Our approach is implemented to support various modalities, orientations, and body regions using global features that model gray scale, texture, structure, and global shape characteristics. The resulting extended query refinement has a significant impact for medical CBIR applications.
Subject(s)
Information Storage and Retrieval/methods , Internet/statistics & numerical data , Radiographic Image Interpretation, Computer-Assisted , Radiology Information Systems/instrumentation , User-Computer Interface , Computer Graphics , Databases, Factual , Diagnostic Imaging/methods , Humans , Medical Informatics Applications , Pattern Recognition, Automated , Sensitivity and Specificity , Software DesignABSTRACT
This paper presents a technical framework to support the development and installation of system for content-based image retrieval in medical applications (IRMA). A strict separation of feature extraction, feature storage, feature comparison, and the user interfaces is suggested. This allows to reuse implemented components in different retrieval algorithms, which improves software quality, shortens the development cycle for applications, and allows to introduce standardized end-user interfaces. Based on the proposed framework, the IRMA engine has been established, which is currently used to evaluate content-based retrieval methods on a collection of 20,000 medical and 135,000 non-medical images.
Subject(s)
Database Management Systems , Information Storage and Retrieval/methods , Radiology Information Systems , Algorithms , HumansABSTRACT
This work presents mechanisms to support the development and installation of content-based image retrieval in medical applications (IRMA). A strict separation of feature extraction, feature storage, feature comparison, and the user interfaces is suggested. The concept and implementation of a system following these guidelines is described. The system allows to reuse implemented components in different retrieval algorithms, which improves software quality, shortens the development cycle for applications, and allows to establish standardized end-user interfaces.
Subject(s)
Information Storage and Retrieval , User-Computer Interface , Algorithms , SoftwareABSTRACT
Categorization of medical images means selecting the appropriate class for a given image out of a set of pre-defined categories. This is an important step for data mining and content-based image retrieval (CBIR). So far, published approaches are capable to distinguish up to 10 categories. In this paper, we evaluate automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined. Based on 6231 reference images from hospital routine, 85.5% correctness is obtained combining global texture features with scaled images. With a frequency of 97.7%, the correct class is within the best ten matches, which is sufficient for medical CBIR applications.
Subject(s)
Diagnostic Imaging , Information Storage and Retrieval , Automation , GermanyABSTRACT
The impact of content-based access to medical images is frequently reported but existing systems are designed for only a particular modality or context of diagnosis. Contrarily, our concept of image retrieval in medical applications (IRMA) aims at a general structure for semantic content analysis that is suitable for numerous applications in case-based reasoning or evidence-based medicine. Within IRMA, stepwise processing results in six layers of information modeling (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer) incorporating medical expert knowledge. At the scheme layer, medical images are represented by a hierarchical structure of ellipses (blobs) describing image regions. Hence, image retrieval transforms to graph matching. The multilayer processing is implemented using a distributed system designed with only three core elements. The central database holds program sources, process-ing schemes, images, features, and blob trees; the scheduler balances distributed computing by addressing daemons running on all connected workstations; and the web server provides graphical user interfaces for data entry and retrieval..
