The AN IMAGE RETRIVAL ANALYSIS BY USING A PATTERN SIMILARITY SCHEME

Abstract

In this paper, we propose a novel scheme for efficient content-based medical image retrieval, formalized according to the Patterns for Next generation Database systems (PANDA) framework for pattern representation and management. The proposed scheme involves block-based low-level feature extraction from images followed by the clustering of the feature space to form higher-level, semantically meaningful patterns. The clustering of the feature space is realized by an expectation–maximization algorithm that uses an iterative approach to automatically determine the number of clusters. Then, the 2-component property of PANDA is exploited: the similarity between two clusters is estimated as a function of the similarity of both their structures and the measure components. Experiments were performed on a large set of reference radiographic images, using different kinds of features to encode the low-level image content. Through this experimentation, it is shown that the proposed scheme can be
efficiently and effectively applied for medical image retrieval from large databases, providing unsupervised semantic interpretation of the results, which can be further extended by knowledge representation methodologies. Forum (CLEF). Since 2004, a medical image retrieval task has been added. Goal is to create databases of a realistic and useful size and also query topics that are based on real{world needs in the medical domain but still correspond to the limited capabilities of purely visual retrieval at the moment. Goal is to direct the research onto real applications and towards real clinical problems to give researchers who are not directly linked to medical facilities a possibility to work on the interesting problem of medical image retrieval based on real data sets and problems. The missing link between computer science research departments and clinical routine is one of the biggest problems that becomes evident when reading much of the current literature on medical image retrieval. Most databases are
extremely small, the treated problems often far from clinical reality, and there is no integration of the prototypes into a hospital infrastructure. Only few retrieval articles specifically mention problems related to the DICOM format (Digital Imaging and Communications in Medicine) and the sheer amount of data that needs to be treated in an image archive (> 30:000 images per day in the Geneva radiology