A new way for multidimensional medical data management : volume of interest (VOI)-based retrieval of medical images with visual and functional features

Author name used in this publication: Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Information technology applications in biomedical functional imaging

Author name used in this publication: (David) Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringVersion of RecordPublishe

Content based retrieval of PET neurological images

Medical image management has posed challenges to many researchers, especially when the images have to be indexed and retrieved using their visual content that is meaningful to clinicians. In this study, an image retrieval system has been developed for 3D brain PET (Position emission tomography) images. It has been found that PET neurological images can be retrieved based upon their diagnostic status using only data pertaining to their content, and predominantly the visual content. During the study PET scans are spatially normalized, using existing techniques, and their visual data is quantified. The mid-sagittal-plane of each individual 3D PET scan is found and then utilized in the detection of abnormal asymmetries, such as tumours or physical injuries. All the asymmetries detected are referenced to the Talairarch and Tournoux anatomical atlas. The Cartesian co- ordinates in Talairarch space, of detected lesion, are employed along with the associated anatomical structure(s) as the indices within the content based image retrieval system. The anatomical atlas is then also utilized to isolate distinct anatomical areas that are related to a number of neurodegenerative disorders. After segmentation of the anatomical regions of interest algorithms are applied to characterize the texture of brain intensity using Gabor filters and to elucidate the mean index ratio of activation levels. These measurements are combined to produce a single feature vector that is incorporated into the content based image retrieval system. Experimental results on images with known diagnoses show that physical lesions such as head injuries and tumours can be, to a certain extent, detected correctly. Images with correctly detected and measured lesion are then retrieved from the database of images when a query pertains to the measured locale. Images with neurodegenerative disorder patterns have been indexed and retrieved via texture-based features. Retrieval accuracy is increased, for images from patients diagnosed with dementia, by combining the texture feature and mean index ratio value

The State of the Art of Medical Imaging Technology: from Creation to Archive and Back

Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable when new tools for data mining and new media for data storing become available. This paper provides a summative account on the creation of medical imaging tomography, the development of image archiving systems and the innovation from the existing acquired image data pools. The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical images with variety of domains and different dimensions. In terms of novelties, the facilities of CBIR for 3D images coupled with image annotation in a fully automatic fashion have been developed and implemented in the system, resonating with future versatile, flexible and sustainable medical image databases that can reap new innovations