Experiments on geometric image enhancement

Sapiro, Guillermo; Tannenbaum, Allen; You, Yu-Li; Kaveh, M.

doi:10.1109/icip.1994.413615

Cited by 15 publications

(7 citation statements)

References 26 publications

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“…Notice that we successfully find the contour in a very noisy environment. Because of the noise, we presmoothed the image using ten iterations of the affine curve shortening nonlinear filter [2], [3], [43], [44]. The cyst boundary was found in 75 steps which took about 5 s. 6) In Fig.…”

Section: A Contour Extraction Resultsmentioning

confidence: 99%

A geometric snake model for segmentation of medical imagery

Yezzi

Kichenassamy

Kumar

et al. 1997

IEEE Trans. Med. Imaging

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Abstract-In this note, we employ the new geometric active contour models formulated in [25] and [26] for edge detection and segmentation of magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound medical imagery. Our method is based on defining feature-based metrics on a given image which in turn leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a potential well. Thus, the snake is attracted very quickly and efficiently to the desired feature.

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Section: A Contour Extraction Resultsmentioning

confidence: 99%

A geometric snake model for segmentation of medical imagery

Yezzi

Kichenassamy

Kumar

et al. 1997

IEEE Trans. Med. Imaging

Self Cite

488

299

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“…Most of the use of PDEs for image processing was done for image debluring or denoising, see for example [1,2,7,14,20,24,26,30,35,36]. In this case, the basic idea is to perform anisotropic diffusion.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, for specific tasks, the exact desirable distribution can be dictated by the application, and the technique here presented applies as well. After this basic equation is presented and analized, we combine it with the smoothing operators proposed in [35] and in [30], obtaining contrast normalization and denoising at the same time. We also extend the flow to local contrast enhancement both in the image plane and in the gray-value space.…”

Section: Introductionmentioning

confidence: 99%

Histogram Modification via Differential Equations

Sapiro

Caselles

1997

Journal of Differential Equations

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The explicit use of partial differential equations (PDEs) in image processing became a major research topic in the past years. In this work we present a framework for histogram (pixel-value distribution) modification via ordinary and partial differential equations. In this way, the image contrast is improved. We show that the histogram can be modified to achieve any given distribution as the steady state solution of an image flow. The contrast modification can be performed while simultaneously reducing noise in a unique PDE, avoiding noise sharpening effects of classical algorithms. The approach is extended to local contrast enhancement as well. A variational interpretation of the flow is presented and theoretical results on the existence of solutions are given.1997 Academic Press

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“…Because the simplicity and better efficiency of the histogram based algorithms, these algorithms are widely used for contrast enhancement of images. Also it should be mentioned that histogram based techniques are much less expensive when compare to the other methods by Sapiro et al (1994) Ziaei et al (2008; Jagatheeswari et al (2009);Kachouie (2008);Zhao et al (2010), Ramyashree et al (2010);Vij and Singh (2011);Mahmoud and Marshal (2008). Studies of frequency domain transform mainly concentrate on the speckle reduction and histogram equalization is a moderately typical method of image enhancement in the spatial field.…”

Section: • Spatial Domain Technique • Frequency Domain Techniquementioning

confidence: 99%

An Enhanced Approach for Medical Brain Image Enhancement

V.¹

2012

Journal of Computer Science

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Problem statement: One of the most common degradations in medical images is their poor contrast quality and noise. The DICOM image consists of speckle (multiplicative noise). While the image is enhanced, the multiplicative noise present in the image is also enhanced. Approach: This study describes the hybrid method to improve the image quality of Digital Imaging and Communications in Medicine (DICOM) images. The idea of image enhancement technique is to improve the quality of an image for early diagnosis. Then followed by a noise reduction using speckle reduction anisotropic filter. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image and to reduce the multiplicative noise. Results: In this research study, a new approach for DICOM image is done by applying contrast stretching and anisotropic diffusion where denoising of multiplicative noise is carried out and the level of contrast is improved. The quality of the image is enhanced and noise free for DICOM image analysis. The effectiveness of hybrid method is proved by quantitative approach. Conclusion and Recommendation: The performance of the proposed study is compared with the existing traditional algorithm and real time medical diagnosis image.

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Experiments on geometric image enhancement

Cited by 15 publications

References 26 publications

A geometric snake model for segmentation of medical imagery

A geometric snake model for segmentation of medical imagery

Histogram Modification via Differential Equations

An Enhanced Approach for Medical Brain Image Enhancement

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