With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including text-based and content-based systems....

Content-based image retrieval (CBIR) is a method for searching that finds related images in medical database. Furthermore, clinical adaptation of CBIR hampered part by contextual gap the disparity among person characterization picture and framework image. This technique makes it tough user to validate fetched are similar query addition only fetches top-ranked ignores low-ranking ones. Visual An...

More number of techniques about medical image retrieval is used in medical domain. In our Content Based Medical Image Retrieval System, high level semantics of an image is very important. Regarding with this issue, we proposed a method using co-occurrences matrix to extract texture features and Canny Edge Detection is to extract shape features. Then K-means clustering algorithm and Euclidean di...

Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review sta...

Content-based image retrieval (CBIR) technology benefits not only large image collections management, but also helps clinical care, biomedical research, and education. Digital images are found in X-Rays, MRI, CT which are used for diagnosing and planning treatment schedules. Thus, visual information management is challenging as the data quantity available is huge. Currently, available medical d...

This paper presents the results of the State University of New York at Buffalo in the Cross Language Evaluation Forum 2005 (CLEF 2005). We participated in monolingual Portuguese, bilingual EnglishPortuguese and in the medical image retrieval tasks. We used the SMART retrieval system for text retrieval in the mono and bilingual retrieval tasks on Portuguese documents. The main goal of this part ...

This paper describes the participation of the SNUMedinfo team at the two retrieval tasks (Ad-hoc image-based retrieval and Case-based retrieval) in the ImageCLEF 2013 medical task. For the ad-hoc image-based retrieval task, we submitted 1 baseline textual run using query likelihood model in Indri search engine, and 4 visual runs utilizing various image features implemented in Lire image retriev...