Visualization using rational morphology and zonal magnitude reduction

Kogan, Robert G.; Agaian, Sos S.; Lentz, K.P.

doi:10.1117/12.304595

Cited by 22 publications

(18 citation statements)

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“…Analyzing the existing transform-based enhancement algorithms ( -rooting and magnitude reduction methods [2], [8]), we find a common algorithm, which encompasses all of these techniques. The actual procedure of the signal/image enhancement via an invertible transform consists of the following three steps:…”

Section: B General Transform-based Image Enhancement Algorithmmentioning

confidence: 99%

Transform-based image enhancement algorithms with performance measure

Agaian

Panetta

Grigoryan

2001

IEEE Trans. on Image Process.

356

139

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This paper presents a new class of the "frequency domain"-based signal/image enhancement algorithms including magnitude reduction, log-magnitude reduction, iterative magnitude and a log-reduction zonal magnitude technique. These algorithms are described and applied for detection and visualization of objects within an image. The new technique is based on the so-called sequency ordered orthogonal transforms, which include the well-known Fourier, Hartley, cosine, and Hadamard transforms, as well as new enhancement parametric operators. A wide range of image characteristics can be obtained from a single transform, by varying the parameters of the operators. We also introduce a quantifying method to measure signal/image enhancement called EME. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms.

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Section: B General Transform-based Image Enhancement Algorithmmentioning

confidence: 99%

Transform-based image enhancement algorithms with performance measure

Agaian

Panetta

Grigoryan

2001

IEEE Trans. on Image Process.

356

139

View full text Add to dashboard Cite

show abstract

“…Transform domain enhancement techniques involve mapping the image intensity data into a given transform domain by using transforms such as the 2-D discrete cosine transform (DCT), Fourier transform, and other fast unitary transforms. A survey of the transform-based image enhancement techniques can be found in [3], [4], [22], [25], [27]- [29], [32]. The basic idea in using this technique is to enhance the image by manipulating the transform coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…The basic idea in using this technique is to enhance the image by manipulating the transform coefficients. Recently, many transform-based enhancement techniques have been proposed [3], [18], [22], [28], [29], [32], [35], [36]. One of the more popular and proven enhancement techniques is alpha rooting and its modifications [3], [4], [28], [29], [31], [32], [36].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many transform-based enhancement techniques have been proposed [3], [18], [22], [28], [29], [32], [35], [36]. One of the more popular and proven enhancement techniques is alpha rooting and its modifications [3], [4], [28], [29], [31], [32], [36]. The basic limitations of the transform-based image enhancement methods are: 1) they introduce certain artifacts which Aghagolzadeh and Ersoy called "objectionable blocking effects" [32]; 2) they cannot simultaneously enhance all parts of the image very well; 3) it is difficult to automate the image enhancement procedure.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy

Agaian

Silver

Panetta

2007

IEEE Trans. on Image Process.

Self Cite

479

203

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Many applications of histograms for the purposes of image processing are well known. However, applying this process to the transform domain by way of a transform coefficient histogram has not yet been fully explored. This paper proposes three methods of image enhancement: a) logarithmic transform histogram matching, b) logarithmic transform histogram shifting, and c) logarithmic transform histogram shaping using Gaussian distributions. They are based on the properties of the logarithmic transform domain histogram and histogram equalization. The presented algorithms use the fact that the relationship between stimulus and perception is logarithmic and afford a marriage between enhancement qualities and computational efficiency. A human visual system-based quantitative measurement of image contrast improvement is also defined. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms.

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“…While a large set of surveys of the different methods for image enhancements can be found in [2,58,31,68,94,66,4,1,3,71]. This section covers some of the most common image enhancement methods.…”

Section: Image Enhancementmentioning

confidence: 99%

MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

Hernadez¹

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In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices.Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines.The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers.We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be v developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain.In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem.In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

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Visualization using rational morphology and zonal magnitude reduction

Cited by 22 publications

References 0 publications

Transform-based image enhancement algorithms with performance measure

Transform-based image enhancement algorithms with performance measure

Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy

MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

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