Comparative Evaluation of Ultrasound Kidney Image Enhancement Techniques

Hafizah, Wan Mahani; Supriyanto, Eko

doi:10.5120/2524-3432

Cited by 26 publications

(19 citation statements)

References 15 publications

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“…It has defeated the drawbacks of the wavelet transform in the image noise and has solved the sparsity problem of smooth edge target (Hafizah and Supriyanto, 2011).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Digital Images Through Cuckoo Search Algorithm in Combination with Morphological Operation

Babu¹,

Sunitha²

2015

Journal of Computer Science

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Abstract:This study presents an image enhancement approach to Cuckoo Search Algorithmin with Morphological Operation. At the present time, in many image processing applications digital images are developed. Machine vision, computer interfaces, manufacturing, compression for storage and more are some of the fields of image processing application. Before using it in any applications the image has to be managed, such processing is said to be image enhancement. We propose a method to combine with an enhancing digital images through cuckoo search algorithmin and morphological operation. Therefore, the appearance of noise produces distortion in an image and thus the image will be unattractive. This decreases the discernibility of many features inside the images. In this study, we are working to overcome this drawback by getting an improved contrast value after converting the color image into grayscale image. The fundamental characteristic of this CS algorithm is that the amplitudes of its components can objectively reflect the contribution of the gray levels to the representation of image information for the best contrast value of an image. After selecting the best contrast value of an image in CS algorithm, morphological operations have to be done. In morphological operations, the intensity parameters of the image are adjusted to improve its quality. Experimental results demonstrate that the proposed approach is converted into original color image without noise and adaptive process to enhance the quality of images.

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“…It has defeated the drawbacks of the wavelet transform in the image noise and has solved the sparsity problem of smooth edge target (Hafizah and Supriyanto, 2011).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Digital Images Through Cuckoo Search Algorithm in Combination with Morphological Operation

Babu¹,

Sunitha²

2015

Journal of Computer Science

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“…In [2] the various filters for SAR images are compared with Wavelet Soft Thresholding in terms of MSE and ENL. In [3] the comparative evaluation was performed on ultrasound kidney images with various Spatial Domain Filters, Frequency Domain Filters, Morphological Processing and Wavelet Filters based on MSE and PSNR. The Morphological Filter performs better in terms of PSNR means that valuable signal component is present in the denoised image.…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Speckle Reduction Filters for Ultrasound Kidney Images

Thiyagarajan¹

2012

Engineering and Applied Science

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There are multiple medical imaging modalities are available like X-ray, Ultrasound, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) etc. Among these, ultrasound imaging is widely used because of its non-invasive nature and low acquisition cost. The main problem in ultrasound imaging is the presence of speckle noise which degrades the quality of images due to coherent waves. The different types of filters such as adaptive, non-adaptive and multi scale are used quite often for image improving. This research paper is focused on performance comparison analysis among various existing despeckle filters based on the RMSE, PSNR and MSSIM value. The ultrasound kidney images are used to evaluate the filters discussed in this paper.

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“…Histogram equalization is used to improve the visual appearance of an image by adjusting the image histogram [11], [12] . Fig.…”

Section: ) Histogram Equalizationmentioning

confidence: 99%

Classification of Ultrasound Kidney Images using PCA and Neural Networks

Attia¹,

Abou-Chadi²,

Moustafa³

et al. 2015

ijacsa

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Comparative Evaluation of Ultrasound Kidney Image Enhancement Techniques

Cited by 26 publications

References 15 publications

Enhancing Digital Images Through Cuckoo Search Algorithm in Combination with Morphological Operation

Enhancing Digital Images Through Cuckoo Search Algorithm in Combination with Morphological Operation

Comparative Evaluation of Speckle Reduction Filters for Ultrasound Kidney Images

Classification of Ultrasound Kidney Images using PCA and Neural Networks

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