Digital-tape-based mass image storage and retrieval system

1990 ◽  
Author(s):  
Hava Volterra
Keyword(s):  
Retrieval System ◽  
Image Storage ◽  
Storage And Retrieval ◽  
Image Storage And Retrieval

Image receptors

2018 ◽  
Author(s):  
Jim Hughes
Keyword(s):  
Radiation Dose ◽  
Real Time ◽  
Image Intensifier ◽  
Flat Panel ◽  
Visible Image ◽  
Image Storage ◽  
X Ray ◽  
Storage And Retrieval ◽  
Reduce Radiation Dose ◽  
Image Storage And Retrieval

The receptor head is the system that converts the X-ray beam into a visible image and allows it to be displayed. Modern systems accomplish this by using either an image intensifier (II) or a flat-panel detector (FPD). Both allow real-time fluoroscopy, as well as last-image hold, image storage and retrieval, and other features to assist in procedures or reduce radiation dose. This chapter covers the design and functions of image receptor heads used on C-arm systems that produce images from the incident X-ray beam. This includes the process of intensification and amplification of the image within an II system, as well as the function and the use of newer FPD systems.

A Hybrid Approach to Content-Based Image Retrieval

2013 ◽  
pp. 91-104
Author(s):  
Görkem Asilioglu ◽  
Emine Merve Kaya ◽  
Duygu Sarikaya ◽  
Shang Gao ◽  
Tansel Ozyer ◽  
...  
Keyword(s):  
Hybrid Approach ◽  
Input Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Test Results ◽  
Image Storage ◽  
Storage And Retrieval ◽  
Image Storage And Retrieval ◽  
Traditional Approaches ◽  
Distance Sets

Digital image storage and retrieval is gaining more popularity due to the rapidly advancing technology and the large number of vital applications, in addition to flexibility in managing personal collections of images. Traditional approaches employ keyword based indexing which is not very effective. Content based methods are more attractive though challenging and require considerable effort for automated feature extraction. In this chapter, we present a hybrid method for extracting features from images using a combination of already established methods, allowing them to be compared to a given input image as seen in other query-by-example methods. First, the image features are calculated using Edge Orientation Autocorrelograms and Color Correlograms. Then, distances of the images to the original image will be calculated using the L1 distance feature separately for both features. The distance sets will then be merged according to a weight supplied by the user. The reported test results demonstrate the applicability and effectiveness of the proposed approach.

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