Comparing Non-homomorphic and Homomorphic Wavelet Filtering Techniques for Speckled Images

Gungor, M. A.; Karagoz, I.

doi:10.7763/ijcte.2016.v8.1077

Cited by 3 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the help of wavelet coefficients, inverse discrete wavelet transform is performed to get a reconstructed image. The exponential of this reconstructed image is the filtered output [18]. In this work, filtering is done in two levels.…”

Section: Homomorphic Wavelet Filtermentioning

confidence: 99%

A Comparison on Speckle Noise Suppression Algorithm for Intravascular Ultrasound (Ivus) Images

Archana¹,

Vanithamani²

2021

INDJCSE

View full text Add to dashboard Cite

Accurate and reliable diagnosis of carotid artery stenosis depends on the quality of IVUS images. Especially in ultrasound images where coherent sources are involved, speckle noise causes blurring and loss of information. Thus, methods to eliminate speckle noise plays an essential part in the field of medical imaging. This paper compares various speckle noise suppression algorithms for carotid artery ultrasound images. Speckle noise reduction algorithms that are implemented includes Homomorphic Wavelet Level 1 and Level 2, Perona-Malik (PM) filter, Modified PM1, Modified PM2, Adaptive PM, Butterworth Filter, Doubly Degenerative Diffusion (DDD), Speckle Reducing Anisotropic Diffusion (SRAD) and Total Variance (TV) filter. A quantitative evaluation is carried out by estimating Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Beta Metric and Natural Image Quality Evaluator (NIQE). The performance metrics shows that Homomorphic Wavelet Level1, Modified PM 2, Adaptive PM and SRAD are robust in eliminating speckle noise from carotid artery ultrasound images, thereby increasing its diagnostic accuracy. Though DDD and TV approach have good SNR and PSNR values, their low Beta metric and high NIQE values have made them ineffective.

show abstract

Section: Homomorphic Wavelet Filtermentioning

confidence: 99%

A Comparison on Speckle Noise Suppression Algorithm for Intravascular Ultrasound (Ivus) Images

Archana¹,

Vanithamani²

2021

INDJCSE

View full text Add to dashboard Cite

show abstract

Design and Optimization of Homomorphic Medical Image Fusion Algorithm

Awang

Marzuki

Kamil

2021

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

View full text Add to dashboard Cite

Computer-Aided Diagnosis Approaches to Fatty Liver Disease According to Sonographic Images Based on Wavelet Transform: A Review Study

2018

JCRC

View full text Add to dashboard Cite

Introduction: Fatty liver is usually diagnosed by ultrasound, but this diagnosis can be difficult because the disease does not always lead to abnormal conditions on gray levels that can be detected by the eye. However, ultrasound is still the first choice to detect fatty liver due to its low cost and availability, and the lack of side effects. The study reviewed Computer-Aided Diagnosis approaches to fatty liver disease, based on wavelet transform sonographic image processing. Methods: In this review study, a search was conducted based on related keywords and articles that had been published in English over the last 12 years. The findings were extracted based on the aim of study. Findings: Nowadays wavelet transformation has been widely used in the field of medical image processing because of its adaptability to the characteristics of the human eye system. The well-known wavelets used to liver diseases detection include Haar, Symlet, Daubechies and Gabor. Extracting the proper properties of images plays an important role in detecting diseases. Important statistical features of image textures are: statistical descriptors based on the intensity histogram and the GLCM matrix (Gray level Co-occurrence Matrix). The popular algorithms used for liver disease include neural network, Support Vector Machine (SVM), Bayesian, decision tree, K-Nearest Neighbor (KNN), and regression. Conclusion: The sensitivity, specificity and accuracy of the extracted statistical features of the output components of wavelet transform are generally better than those obtained from the original image itself. Gabor’s wavelet transformation often has a higher efficiency than the Daubechies and Symlet wavelet transforms because the two transforms only break up the halfband of low frequencies and lose some of the intermediate frequency regions, while Gabor retains all of the frequency regions This precision also mainly depends on the type of features selected and the type of classification. Statistical features based on intensity histograms do not provide relative information about the spatial of pixels relative to each other. To enter this spatial information of pixels in a texture analysis, it is recommended to use GLCM matrix in gray images. The type of classifier used can significantly impact on the precision of the final diagnosis.

show abstract

Comparing Non-homomorphic and Homomorphic Wavelet Filtering Techniques for Speckled Images

Cited by 3 publications

References 20 publications

A Comparison on Speckle Noise Suppression Algorithm for Intravascular Ultrasound (Ivus) Images

A Comparison on Speckle Noise Suppression Algorithm for Intravascular Ultrasound (Ivus) Images

Design and Optimization of Homomorphic Medical Image Fusion Algorithm

Computer-Aided Diagnosis Approaches to Fatty Liver Disease According to Sonographic Images Based on Wavelet Transform: A Review Study

Contact Info

Product

Resources

About