Image denoising using multiresolution principal component analysis

Malini, S.; Moni, R. S.

doi:10.1109/gcct.2015.7342613

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…Under unavoided circumstances, in medical images (US, MRI, X-Ray...), because of the generation wave nature, noise gets introduced in these images. The preprocessing step in images removes noise, which is an important step, to get better results of image processing in higher level such as secure image communication, fusion, recognition, identification, segmentation, compression [6].…”

Section: Introductionmentioning

confidence: 99%

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Medical Image Retrieval using Dual Tree Complex Wavelet Transform and Principal Component Analysis with Haralick Texture Features

C.,

K.*

2020

IJRTE

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Noise and distortion occurs in all types of medical images (Computed Tomography (CT), Magnetic Resonance Imaging (MRI.)..) and are unavoidable during the stages of image acquisition. We use medical image retrieval to extract the images from database by texture, shaptrix or color features. We use Dual Tree Complex Wavelet Transform (DTCWT) and Principal Component Analysis (PCA). DTCWT extracts the information of images. PCA compress the images. It also minimizes the feature vectors dimensions of all images. Haralick texture features are extracted from images with the co-occurrence matrix. This matrix describes the relationship of pixels. The similar images are found by calculating the similarity measure of the query image and all images in database by Mahalanobis distance. This method retrieves the similar images from database with respect to the input image provided by the user. The performance of the proposed algorithm can be found by precision and recall measures for evaluation. This system can be used in hospitals, clinics etc., for detecting diseases earlier

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Section: Introductionmentioning

confidence: 99%

“…SSIM and PSNR ratio was calculated for four images. The results of the denoising by the wavelet thresholding are provided [6].…”

Section: Introductionmentioning

confidence: 99%