Explicit Study on Design and Development of Content-based Image Retrieval in Medical Imaging
Digital Image Databases and documentation provide lot of research areas. Significant among them is, the Content Based Image Retrieval (CBIR) research area for manipulating large amount of image databases and archives. The development in the field of medical imaging system has lead industries to conceptualize a complete automated system for the medical procedures, diagnosis, treatment and prediction. There is a continuous research in the area of CBIR systems typically for medical images, which provides a successive algorithm development for achieving generalized methodologies, which could be widely used. The achievement of such system mainly depends upon the strength, accuracy and speed of the retrieval systems. Content Based Image Retrieval (CBIR) system is valuable in medical systems as it provides retrieval of the images from the large dataset based on similarities. The aim of this paper is to discuss the various techniques, the assumptions and its scope suggested by various researchers and setup a further roadmap of the research in the field of CBIR system for medical image.
How to cite this article:
Fatima S. Explicit Study on Design and Development of Content-based Image Retrieval in Medical Imaging. J Adv Res Electro Engi Tech 2021; 8(1&2): 23-27.
2. Dan I, Bogdan I, Shahidul I et al. Using Depth Measuring Cameras for a New Human Computer Interaction inAugmented Virtual Reality Environments.
3. Dan I, Bogdan I, Shahidul I et al. Multimodal Control of Virtual Game Environments Through Gestures and PhysicalControllers. 978-1-61284-890-7/11/$26.00©2011 IEEE.
4. Pedro T, Jorge L. Hand gesture recognition using color and depth images enhanced with hand angular pose data. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). 2012; 13-15.
5. González-Ortega D, Díaz-Pernas FJ, Martínez-Zarzuela M et al. A Kinect-based system for cognitive rehabilitation exercises monitoring.
6. Giovanni D, Leone A, Siciliano P. Human posture recognition with a time-of-flight 3D sensor for in home applications. 0957-4174/$ -see front matter _ 2012 Elsevier Ltd. All rights reserved.
7. Xiaodong Y, Ying LT. Effective 3D action recognition using Eigen Joints. 1047-3203/$ - see front matter _2013 Elsevier Inc. All rights reserved.
8. Hafiz AR, Amin MF, Murase K. Using Complex-Valued Levenberg-Marquardt Algorithm for Learning and Recognizing Various Hand Gestures. WCCI IEEE World Congress on Computational Intelligence. 2012; 10-15.
9. Manjuatha MB, Pradeepkumar BP, Santhosh SY. Survey Paper on Hand Gesture Recognition. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE) 2014; 3(4).
10. Manjuatha MB, Pradeepkumar BP, Santhosh SY. Survey on Skeleton Gesture Recognition Provided by Kinect. International Journal of AdvancedResearch in Electrical, Electronics and Instrumentation Engineering (IJAREEIE) 2014; 3(4). ISSN: 2278-8875.
11. Giorgio G. A Nearest-Neighbor Approach to Relevance Feedback in Content Based Image Retrieval.
12. Zhang L, Zhezhou Y, Zhou C et al. Image Retrieval Using Multi-Granularity Color Features. ICALIP, IEEE, 2008.