Pulmonary CT image denoising algorithm based on curvelet transform criterion

Shi, Zhengang; Qin-zi, Li

doi:10.1109/mape.2017.8250909

Cited by 3 publications

(2 citation statements)

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“…Curve-let transform is a multi-scale geometric transform that can signify edges and curves originalities more competently than traditional wavelets as wavelets have the disadvantage of poor directionality [8][10] [22] [23]. Although the complex wavelet transform is proof for directional selectivity, it is difficult to design complex wavelets with good filter characteristics and perfect reconstruction properties [16] [18].…”

Section: Fast Discrete Curvelet Transformmentioning

confidence: 99%

Medical Image Denoising using Fast Discrete Curvelet Transform

Anandan¹

2020

IJETER

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Now-a-days, medical field plays a very crucial role in our daily life, as a part of it MRI (Magnetic resonance imaging) scans, CT (computed tomography) images, Ultrasound images etc. of the victim which are one of the main things that are to be determined correctly based on which the patient's condition is concluded and treated. The main problem here occurs is for the original image where the image gets noisy and the features of the original image are lost due to many factors. So, here in our paper, we instigate the method of image denoising technique which helps to eliminate the noisy observations and other disturbances and reconstructs the original image very accurately. The image denoising is one of the important preprocessing steps in medical field image processing analysis. For this denoising method, we are going to use the Fast Discrete Curvelet Transform which is a multi-scale geometric transform and is designed to signify the image or video sequences at different scales and angles. also the performances of it by using fast Fourier discrete curve-let transform which is based on ridge-let analysis theory for denoising procedures and makes recommendations with the help of adaptive threshold algorithm which is applied on the image and gets the original image with effectiveness also retrieves the important detail features in the image and also the quality of the image to be recovered by using the parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE).

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Section: Fast Discrete Curvelet Transformmentioning

confidence: 99%

Medical Image Denoising using Fast Discrete Curvelet Transform

Anandan¹

2020

IJETER

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“…For denoising in spatial domain, linear and non-linear filters [5,6,7] anisotropic diffusion [8,9] and total variation methods [10], dictionary learning method [11,12], bilateral and non-local means (NLM) filters [13], Neural Networks [14,15] and deep learning algorithms [16,17,18] have been used. For transform domain filtering, Wavelet based denoising [19,20], fourier based denoising [21,22], curvelet based denosinig [23,24], threshold estimation [25] and shrinkage rules [26,27] have been used.…”

Section: Introductionmentioning

confidence: 99%

Düzenlenmiş Gri Kurt Optimizasyon Algoritması ile Gürültü Temizleme

Ardaç

Erdoğmuş

2018

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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In this study, image denoising has been realized with with the one of the recent Nature-Inspired optimization algorithms, Grey Wolf Optimizer(GWO). GWO is one of the recent most studied continous optimization algorithm which performs better than the other algorithms. In this study, ten test images have been selected and gaussian noise has been added with some variance values. After the noisy images have been attained, these noisy images have been filtered with convulation in spatial domain. Filter coefficents have been trained with GWO, Modified Grey Wolf Optimizer(MGWO) and Genetic Algorithm(GA). Weiner filtering is also applied on the images for image denosing. The results show that Weiner Filter outperforms GWO trained filters on most of the images. MGWO performance is better then GWO and the results show that MGWO can also be used as an alternative method for image denoising. In the future studies, adaptive MGWO can be enhanced for much more succesfull image denoising process.

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