A new medical image enhancement algorithm using adaptive parameters

The quality of medical images plays a vital role in many image processing applications such as image segmentation, feature extraction, image classification, image recognition, and image fusion. Some of the common problems with Medical images are noise, blur, or low contrast. According to our observa...

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Vydáno v:International journal of imaging systems and technology Ročník 32; číslo 6; s. 2198 - 2218
Hlavní autoři: Dinh, Phu‐Hung, Giang, Nguyen Long
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.11.2022
Wiley Subscription Services, Inc
Témata:
Adaptive algorithms
Algorithms
CLAHE
Computer vision
Decomposition
Edge detection
Feature extraction
Histograms
Image classification
Image contrast
Image enhancement
Image processing
Image quality
Image segmentation
LED
Medical imaging
MPA
Noise reduction
Object recognition
Parameters
STS
Tensors
Texture
ISSN:0899-9457, 1098-1098
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Shrnutí:The quality of medical images plays a vital role in many image processing applications such as image segmentation, feature extraction, image classification, image recognition, and image fusion. Some of the common problems with Medical images are noise, blur, or low contrast. According to our observations, current image enhancement algorithms only focus on solving individual problems such as gray level adjustment, noise reduction, or sharpness enhancement. This paper proposes a novel algorithm to solve problems on images simultaneously. First, we propose an image decomposition algorithm. This algorithm allows decomposing the image into three components: structure (IS), texture (IT), and noise (IN). Second, the structural component (IS) is enhanced by the contrast‐limited adaptive histogram equalization method to obtain ICLAHE. We use the structure tensor salient detection operator and the Laplace edge detection operator to add structural and texture features. These operators are applied to the ICLAHE and IT components to obtain the ISTS and ILED components, respectively. The IT and ILED components are used to generate the enhanced component (called IT_E) by using the Max operator. Third, the Marine predators algorithm is used to find the optimal parameters β1, β2, β3, and β4 corresponding to ICLAHE, ISTS, IT_E, and IN. Finally, the enhanced image is made up of the sum of the ICLAHE, ISTS, IT_E, and IN images multiplied by the optimal parameters β1, β2, β3, and β4, respectively. Six state‐of‐the‐art image enhancement approaches, seven medical image fusion algorithms, and six image quality metrics have been utilized to verify the proposed approach's effectiveness. The experimental results show that the proposed method significantly improves the quality of the input medical images as well as significantly improves the efficiency of current medical image synthesis algorithms.
Bibliografie:Funding information
Thuyloi University Foundation for Science and Technology, Grant/Award Number: TLU.STF.21‐03
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SourceType-Scholarly Journals-1
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ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22778