Use of IRIS image-enhancement facilities on digital images by radiologists during a clinical trial at the Ottawa Civic Hospital

1990 ◽  
Author(s):  
Marjorie Coristine ◽  
Morris Goldberg ◽  
Carolyn Beeton ◽  
Richard F. Dillon ◽  
Jo W. Tombaugh ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Image Enhancement ◽  
Digital Images ◽  
Iris Image ◽  
Ottawa Civic Hospital

Physician acceptance of the IRIS user interface during a clinical trial at the Ottawa Civic Hospital

1990 ◽  
Author(s):  
Marjorie Coristine ◽  
Carolyn Beeton ◽  
Jo W. Tombaugh ◽  
J. Ahuja ◽  
Garry Belanger ◽  
...  
Keyword(s):  
Clinical Trial ◽  
User Interface ◽  
Ottawa Civic Hospital

scholarly journals An Extensive Review on Image Segmentation Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
Kamlesh Sharma ◽  
Nidhi Garg
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Digital Images ◽  
Extensive Review ◽  
Current Image ◽  
Segmentation Image

Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

scholarly journals An Extensive Review on Image Segmentation Techniques

2021 ◽  
pp. 1-5
Author(s):  
Dr. Kamlesh Sharma ◽  
◽  
Nidhi Garg ◽  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Digital Images ◽  
Extensive Review ◽  
Current Image ◽  
Segmentation Image

Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

A Review of Enhancement and Segmentation Techniques for Digital Images

2019 ◽  
Vol 19 (03) ◽  
pp. 1950013 ◽  
Author(s):  
Jasmeen Gill ◽  
Akshay Girdhar ◽  
Tejwant Singh
Keyword(s):  
Pattern Recognition ◽  
Image Enhancement ◽  
Digital Images ◽  
Image Acquisition ◽  
Region Of Interest ◽  
Vast Literature

Image enhancement and segmentation are the two imperative steps while processing digital images. The goal of enhancement is to improve the quality of images so as to nullify the effect of poor illumination conditions during image acquisition. Afterwards, segmentation is performed to extract region of interest (ROI) from the background details of the image. There is a vast literature available for both the techniques. Therefore, this paper is intended to summarize the basic as well as advanced enhancement and segmentation techniques under a single heading; to provide an insight for future researches in the field of pattern recognition.

An Efficient and Automatic Iris Recognition System Using ICM Neural Network

2010 ◽  
pp. 445-460
Author(s):  
Guangzhu Xu ◽  
Yide Ma ◽  
Zaifeng Zhang
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
Iris Recognition ◽  
Hamming Distance ◽  
Image Database ◽  
Recognition System ◽  
Rectangular Region ◽  
Cortical Model ◽  
Image Preprocessing ◽  
Iris Image

Iris recognition has been shown to be very accurate for human identification. In this chapter, an efficient and automatic iris recognition system using Intersecting Cortical Model (ICM) neural network is presented which includes two parts mainly. The first part is image preprocessing which has three steps. First, iris location is implemented based on local areas. Then the localized iris area is normalized into a rectangular region with a fixed size. At last the iris image enhancement is implemented. In the second part, the ICM neural network is used to generate iris codes and the Hamming Distance between two iris codes is calculated to measure the dissimilarity. In order to evaluate the performance of the proposed algorithm, CASIA v1.0 iris image database is used and the recognition results show that the system has good performance.

scholarly journals Experimental Study of Modified Multilevel Median Filter for Noise Reduction

2019 ◽  
pp. 135-139 ◽  
Author(s):  
Eko Hariyanto ◽  
Andysah Putera Utama Siahaan ◽  
Solly Aryza
Keyword(s):  
Experimental Study ◽  
Noise Reduction ◽  
Image Enhancement ◽  
Digital Image ◽  
Digital Images ◽  
Median Filter ◽  
Experiment Study ◽  
Digital Image Enhancement

Digital image enhancement is efforts to improve the quality of a declining image and one of the causes of the decline in the quality of digital images is the emergence of spots called noise. Median filter is one method that is widely used and developed to digital images noise reduction. In this paper, we conducted an experiment study to reduce noise using a standard multilevel median filter and a modified multilevel median filter. Further, we measured the images filtered quality using MSE and PNSR to find out the advantages of both methods.

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