Compressed medical images are widely used in clinical teaching and diagnosis. To save computing cost and storage spaces, research on compressed medical image retrieval is meaningful. This paper proposes a novel medical image retrieval scheme in DCT (Discrete Cosine Transformation) compressed domain. We firstly obtain the multi resolution image by reorganizing the DCT coefficients, then, segment...

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced ...

Relevance feedback is an effective tool to bridge the gap between superficial image contents and medically-relevant sense in content-based medical image retrieval. In this paper, we propose an interactive medical image search framework based on pairwise constraint propagation. The basic idea is to obtain pairwise constraints from user feedback and propagate them to the entire image set to recon...

In this study, we propose a content-based medical image retrieval framework based on binary association rules to augment the results of medical image diagnosis, for supporting clinical decision making. Specifically, this work is employed on scanned Magnetic Resonance brain Images (MRI) and the proposed Content Based Image Retrieval (CBIR) process is for enhancing relevancy rate of retrieved ima...

The rapid expansion and improvement in medical science and technology which generates the amount of image data more and more in its regular activity such as CT-image, X-ray, MRI etc. Due to scalability of image data, it has been increased in size of database and containing millions of images. For the proper management of medical image and requirement of efficient retrieval system for clinical d...

One important challenge in modern Content-Based Medical Image Retrieval (CBMIR) approaches is represented by the semantic gap, related to the complexity of the medical knowledge. Among the methods that are able to close this gap in CBMIR, the use of medical thesauri/ontologies has interesting perspectives due to the possibility of accessing on-line updated relevant webservices and to extract re...

Medical images are critical assets for medical diagnosis, research, and teaching. To facilitate automatic indexing and retrieval of large medical image databases, we propose a structured framework for designing and learning vocabularies of meaningful medical terms with associated visual appearance from image samples. These VisMed terms span a new feature space to represent medical image content...

Combining low-level features that represent the content of medical images with high level features that are saved with images would allow the expansion of text queries submitted to Content Based Image Retrieval (CBIR) systems. Expanding these text queries would allow CBIR systems to respond more effectively to specific queries when retrieving medical images. We hypothesized that adding an autom...

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 techniques consists of multiple steps and aim ...