A comparison of similarity measures for digital subtraction radiography

Sovakar, Abhijit; Schmiti, Walter; Repges, Rudolf

doi:10.1016/s0010-4825(97)83769-9

Cited by 19 publications

(13 citation statements)

References 17 publications

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“…Biomedical images require special safety and confidentiality, because critical judgment is done on the information provided by biomedical images. Biomedical imaging is used to create biomedical images of the human body for clinical purpose or medical science including the study of normal anatomy and physiology [17].…”

Section: Biomedical Imagementioning

confidence: 99%

Secure Transmission and Recovery of Embedded Patient Information from Biomedical Images of Different Modalities through a Combination of Cryptography and Watermarking

Koley¹,

Pal²,

Ghosh³

et al. 2014

IJIGSP

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-In this paper a new type of information hiding skill in biomedical images is proposed through a combination of cryptography and digital watermarking to achieve the enhancement in confidential and authenticated data storage and secured transmission. Here patient's name and doctor's name are considered as patient's information which is encrypted using cryptography and embedded in the scan image of that patient through watermarking. RSA algorithm is used for encryption and higher order bit LSB replacement technique is used for embedding the information. The private keys are also embedded in the cover image to have better security and accurate recovery of the hidden information. The outcome of the proposed methodology shows that the hidden information doesn't affect the cover image and it can be recovered efficiently even from several noisy images. The strength of the proposed embedding scheme is also supported by several image quality matrices.

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Section: Biomedical Imagementioning

confidence: 99%

Secure Transmission and Recovery of Embedded Patient Information from Biomedical Images of Different Modalities through a Combination of Cryptography and Watermarking

Koley¹,

Pal²,

Ghosh³

et al. 2014

IJIGSP

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“…The CCC is referred to as a bias-independent measure for two-dimensional discrete data [175]. For extraction of ECAs, the CCC is first used for characterization of the similarity of the individual parts of the image in order to eliminate the local irrelevant region, thus defining the locally-normalized image data.…”

Section: Local Normalization With Cross-covariance Functionmentioning

confidence: 99%

Multimedia Database Retrieval

Muneesawang¹,

Zhang²,

Guan³

2014

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“…where d * is the distance of two non-NaN pixels defined in equation (8). If b i,j = NaN, this will result…”

Section: Neighborhood-based Measuresmentioning

confidence: 99%

“…Image comparison measures can be divided roughly into two categories, both are widely used in different applications [6,[8][9][10]. One category, high level image comparison, incorporates edge detection (see [11][12][13] for oceanographic examples), or other segmentation methods to extract features from images [14].…”

Section: Introductionmentioning

confidence: 99%

Introduction and Assessment of Measures for Quantitative Model-Data Comparison Using Satellite Images

2010

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Satellite observations of the oceans have great potential to improve the quality and predictive power of numerical ocean models and are frequently used in model skill assessment as well as data assimilation. In this study we introduce and compare various measures for the quantitative comparison of satellite images and model output that have not been used in this context before. We devised a series of test to compare their performance, including their sensitivity to noise and missing values, which are ubiquitous in satellite images. Our results show that two of our adapted measures, the Adapted Gray Block distance and the entropic distance D2, perform better than the commonly used root mean square error and image correlation.

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A comparison of similarity measures for digital subtraction radiography

Cited by 19 publications

References 17 publications

Secure Transmission and Recovery of Embedded Patient Information from Biomedical Images of Different Modalities through a Combination of Cryptography and Watermarking

Secure Transmission and Recovery of Embedded Patient Information from Biomedical Images of Different Modalities through a Combination of Cryptography and Watermarking

Multimedia Database Retrieval

Introduction and Assessment of Measures for Quantitative Model-Data Comparison Using Satellite Images

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