On detection of median filtering in digital images

Kirchner, Matthias; Fridrich, Jessica

doi:10.1117/12.839100

Cited by 177 publications

(185 citation statements)

References 19 publications

Supporting

Mentioning

185

Contrasting

Order By: Relevance

“…In pre-processing, the RGB fundus image is transformed into the HSI image space. Afterwards, the technique of median filtering is performed on the intensity band (I-band) in order to suppress noise [16]. Next, the contrast limited adaptive histogram equalization technique is used so as to enhance the contrast in order to avoid over-saturation in the identical regions in the image of the retina [17].…”

Section: Methodsmentioning

confidence: 99%

An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations

Omar

Hossain

Zhang

et al. 2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

View full text Add to dashboard Cite

Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Abstract-Diabetic retinopathy is considered in terms of the presence of exudates which cause vision loss in the areas affected. This study targets the development of an intelligent mobile-based automatic diagnosis integrated with a microscopic lens to identify retinal diseases at initial stage at any time or place. Exudate detection is a significant step in order obtaining an early diagnosis of diabetic retinopathy, and if they are segmented accurately, laser treatment can be applied effectively. Consequently, precise segmentation is the fundamental step in exudate extraction. This paper proposes a technique for exudate segmentation in colour retinal images using morphological operations. In this method, after pre-processing, the optic disc and blood vessels are isolated from the retinal image. Exudates are then segmented by a combination of morphological operations such as the modified regionprops function and a reconstruction technique. The proposed technique is verified against the DIARETDB1 database and achieves 85.39% sensitivity. The proposed technique achieves better exudate detection results in terms of sensitivity than other recent methods reported in the literature. In future work, our system will be deployed to a mobile platform to allow efficient and instant diagnosis.

show abstract

Section: Methodsmentioning

confidence: 99%

An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations

Omar

Hossain

Zhang

et al. 2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

View full text Add to dashboard Cite

show abstract

“…As an example, section 4.2.1 treated malicious image processing, which can be either exploited for explicit tampering purposes (Fig. 5) or to cover forgery traces [56,85]. Those techniques are usually applied on a whole image, which could be, in turn, already a composite: for this reason, methods targeting image processing detection can be assimilated to those that will be described in the following.…”

Section: Tampering Detection Independent On the Type Of Forgerymentioning

confidence: 99%

“…Kirchner and Fridrich [56] targeted the median filter attack perpetrated in [54], also arguing that since many detectors rely on the detection of linearities, the use of median filtering can be a general symptom of tampering. The authors propose to inspect image by measuring streaking artifacts, typically originated from the application of median filters.…”

Section: Countering Counter-forensicsmentioning

confidence: 99%

Digital image forensics: a booklet for beginners

Redi

Taktak

Dugelay

2010

Multimed Tools Appl

307

115

View full text Add to dashboard Cite

Digital visual media represent nowadays one of the principal means for communication. Lately, the reliability of digital visual information has been questioned, due to the ease in counterfeiting both its origin and content. Digital image forensics is a brand new research field which aims at validating the authenticity of images by recovering information about their history. Two main problems are addressed: the identification of the imaging device that captured the image, and the detection of traces of forgeries. Nowadays, thanks to the promising results attained by early studies and to the always growing number of applications, digital image forensics represents an appealing investigation domain for many researchers. This survey is designed for scholars and IT professionals approaching this field, reviewing existing tools and providing a view on the past, the present and the future of digital image forensics.

show abstract

“…The MF works by moving through the image pixel by pixel, replacing each value with the median value of neighboring pixels. The MF algorithm is defined as (5), which proposed by (Kirchnera and Fridrich, 2010) Equation 5:…”

Section: Noise Removalmentioning

confidence: 99%

The Clinical Applications for Automatic Detection of Exudates

Wisaeng¹,

Hiransakolwong²,

Pothiruk³

2014

American Journal of Applied Sciences

View full text Add to dashboard Cite

Nowadays, the retinal imaging technology has been widely used for segmenting and detecting the exudates in diabetic retinopathy patients. Unfortunately, the retinal images in Thailand are poorquality images. Therefore, detecting of exudates in a large number by screening programs, are very expensive in professional time and may cause human error. In this study, the clinical applications for detection of exudates from the poor quality retinal image are presented. An application incorporating function, including retinal color normalization, contrast enhancement, noise removal, color space selection and removal of the optic disc, was also designed to standardize the workflow of retinal analysis. Afterward, detection of exudate based on optimal global thresholding and improved adaptive Otsu's algorithm was applied. Two experiments were conducted to validate the detection performance with local databases and a publicly available DIARETDB1 database. The first experiment showed the average sensitivity, specificity and accuracy of 93.8, 95.3 and 94.9%, respectively. The cross validation results of the second experiment, 60% (53) of the retinal images were used for training and 40% (36) for testing, the sensitivity, specificity and accuracy are 84.2, 85.9 and 85.2%, respectively. This result indicates the proposed clinical application provides an effective tool in the screening of exudates.

show abstract

On detection of median filtering in digital images

Cited by 177 publications

References 19 publications

An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations

An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations

Digital image forensics: a booklet for beginners

The Clinical Applications for Automatic Detection of Exudates

Contact Info

Product

Resources

About