A Novel Edge Enhancement Method for Ultrasound Imaging

Wang, Bo; Liu, D.C.

doi:10.1109/icbbe.2008.936

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…Of course, these distributions are due to previous logarithmic compression step. However, reference [7] has shown that each distribution can be achieved after applying logarithmic compression. For the first area (fully developed speckle), Raleigh distribution convert into double exponential distribution that its mean and variance are obtained as follows:…”

Section: Definitionmentioning

confidence: 99%

“…We can recognize edges, and segment image by means of proper function via prior analysis and relation between distributions and valid local histogram range, without high computation load of statistic calculations. Therefore, we use local histogram range image definition [7]: at first we should choose proper size for moving window that suits our approach. The experiment on several ultrasound images shows that the best window size for segmentation is 44 × to 1515 × .…”

Section: 23mentioning

confidence: 99%

“…However, its complexity and computational load are high. In [7], researchers defined "local histogram range image" (LHRI) based on histogram distribution which reduced statistic complexity. They applied a classification method to recognize edge or border of organ while, speckle noises remained unchanged.…”

Section: Introductionmentioning

confidence: 99%

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Medical ultrasound image segmentation by modified local histogram range image method

Kermani¹,

Ayatollahi²,

Mirzaei³

et al. 2010

JBiSE

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Fast and satisfied medical ultrasound segmentation is known to be difficult due to speckle noises and other artificial effects. Since speckle noise is formed from random signals which are emitted by an ultrasound system, we can’t encounter the same way as other image noises. Lack of information in ultrasound images is another problem. Thus, segmentation results may not be accurate enough by means of customary image segmentation methods. Those methods that can specify undesirable effects and segment them by eliminating artificial effects, should be chosen. It seems to be a complicated work with high computational load. The current study presents a different approach to ultrasound image segmentation that relies mainly on local evaluation, named as local histogram range image method which is modified by means of discrete wavelet transform. Thus, a significant decrease in computational load is then achieved. The results show that it is possible for tissues to be segmented correctly

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Section: Definitionmentioning

confidence: 99%

Section: 23mentioning

confidence: 99%

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Medical ultrasound image segmentation by modified local histogram range image method

Kermani¹,

Ayatollahi²,

Mirzaei³

et al. 2010

JBiSE

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show abstract

“…Another challenge in ultrasound image enhancement is the enhancement of weak borders caused by steering beams because the beam line is not perpendicular to the orientation of the local edge (Wang and Liu 2008). Meanwhile, the boundaries of interest in an ultrasound image are discontinuities between tissue layers, which are large on the scales of both the wavelength of interrogation and scan line spacing.…”

Section: Introductionmentioning

confidence: 98%

Bas-Relief Map Using Texture Analysis with Application to Live Enhancement of Ultrasound Images

Ma²,

Wang

et al. 2015

Ultrasound in Medicine & Biology

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“…As a result, image processing for reducing the speckle noise and blurring is a critical pre-processing step. However, speckle is not a genuine noise because its texture often carries certain information about the image being viewed [34] and it can be useful for detecting and differentiating different type of lesions. Sometimes, clinicians are afraid that speckle reduction process may suppress the important details of ultrasound image, so speckle reduction should be designed such that it smoothens the image in a controlled fashion without significant loss of information.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of the ultrasound images by modified anisotropic diffusion method

Mittal

Kumar

Saxena

et al. 2010

Med Biol Eng Comput

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Speckle is a primary factor which degrades the contrast resolution and masks the meaningful texture information present in an ultrasound image. Its presence severely hampers the interpretation and analysis of ultrasound images. When speckle reduction technique is applied for visual enhancement of ultrasound images, it is to be kept in mind that blurring associated with speckle reduction should be less and fine details are properly enhanced. With these points in consideration, the modified speckle reduction anisotropic diffusion (MSRAD) method is proposed in the present study to improve the visual quality of the ultrasound images. In the proposed MSRAD method, the four neighboring pixel template in speckle reduction anisotropic diffusion (SRAD) method of Yu and Acton (IEEE Trans Image Process 11:1260-1270, 2002) have been replaced by a new template of larger number of neighboring pixels to calculate the diffusion term. To enhance visual quality of ultrasound images, nonquadratic regularization (Yu and Yadegar, Proceedings of the IEEE international conference on image processing, 2006) is incorporated with MSRAD method and accordingly changes in parameter settings have been made. The performance of MSRAD method was evaluated using clinical ultrasound images, interpretation by the medical experts and results of MSRAD method by subjective and objective criteria.

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A Novel Edge Enhancement Method for Ultrasound Imaging

Cited by 7 publications

References 12 publications

Medical ultrasound image segmentation by modified local histogram range image method

Medical ultrasound image segmentation by modified local histogram range image method

Bas-Relief Map Using Texture Analysis with Application to Live Enhancement of Ultrasound Images

Enhancement of the ultrasound images by modified anisotropic diffusion method

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