X-let's Atom Combinations for Modeling and Denoising of OCT Images by Modified Morphological Component Analysis

An improved analysis of Optical Coherence Tomography (OCT) images of the retina is of essential importance for the correct diagnosis of retinal abnormalities. Unfortunately, OCT images suffer from noise arising from different sources. In particular, speckle noise caused by the scattering of light wa...

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Vydáno v:IEEE transactions on medical imaging Ročník 43; číslo 2; s. 1
Hlavní autoři: Razavi, Raha, Plonka, Gerlind, Rabbani, Hossein
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Abnormalities
Adaptation models
Adaptive thresholding
Algorithms
Analytical models
Denoising
Dictionaries
Discrete cosine transforms
Feature extraction
Image acquisition
Image processing
Image Processing, Computer-Assisted - methods
Image quality
Mathematical models
Medical imaging
Morphological Component Analysis
Morphology
Noise reduction
Optical Coherence Tomography
Photodegradation
Quality assessment
Retina - diagnostic imaging
Signal-To-Noise Ratio
Tomography, Optical Coherence - methods
Transforms
X-let transforms
ISSN:0278-0062, 1558-254X, 1558-254X
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Popis
Shrnutí:An improved analysis of Optical Coherence Tomography (OCT) images of the retina is of essential importance for the correct diagnosis of retinal abnormalities. Unfortunately, OCT images suffer from noise arising from different sources. In particular, speckle noise caused by the scattering of light waves strongly degrades the quality of OCT image acquisitions. In this paper, we employ a Modified Morphological Component Analysis (MMCA) to provide a new method that separates the image into components that contain different features as texture, piecewise smooth parts, and singularities along curves. Each image component is computed as a sparse representation in a suitable dictionary. To create these dictionaries, we use non-data-adaptive multi-scale (X-let) transforms which have been shown to be well suitable to extract the special OCT image features. In this way, we reach two goals at once. On the one hand, we achieve strongly improved denoising results by applying adaptive local thresholding techniques separately to each image component. The denoising performance outperforms other state-of-the-art denoising algorithms regarding the PSNR as well as no-reference image quality assessments. On the other hand, we obtain a decomposition of the OCT images in well-interpretable image components that can be exploited for further image processing tasks, such as classification.
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2023.3320977