MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion
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MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion Rui Zhu1,2 · Xiongfei Li1,2 · Xiaoli Zhang1,2
· Xiaowei Xu3
Received: 27 September 2019 / Revised: 30 July 2020 / Accepted: 4 August 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Motivation: Medical image enhancement is a crucial part to improve the quality of the images. The excellent visual effects and image quality can help doctors make quick diagnoses. Among medical images, Magnetic Resonance Imaging (MRI) images play a vital role in clinical diagnosis. Its imaging principle highlights the human tissue part ignoring the boundary information sometimes. Moreover, some imaging results lose details in visual due to the low contrast and the quality of the images. To overcome these limitations, we propose an MRI enhancement method based on visual-attention by means of contrast adjustment and illumination component preservation. Description: The proposed framework includes image generation and image fusion to tackle the limitation of a single image. First, we assume an MRI image composed of tissues and details. We design an adaptive attenuation weight matrix based on the input MRI image according to a new definition of pixel energy. Then, an illumination-preserving image is introduced into the model for the attenuated image as compensation. Finally, an effective image fusion decision map calculation method is devised to create an enhanced MRI image with higher contrast and better perceptual quality. Results and conclusion: The experimental results show that it is a more effective enhancement method which has better performance on most of the objective evaluation metrics and stability than other 14 methods as well as maintains the balance between contrast and illumination of enhanced MRI images. Keywords MRI image enhancement · Tissue attenuation · Visual-attention · Image quality · Image fusion Xiaoli Zhang
[email protected] 1
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
2
College of Computer Science and Technology, Jilin University, Changchun 130012, China
3
Department of Information Sciences, University of Arkansas at Little Rock, Little Rock, AR, USA
Multimedia Tools and Applications
1 Introduction As a matter of fact recent advances in imaging techniques increase interest in digital image processing [1]. Among them, inspecting Magnetic Resonance Imaging images is an important step during medical diagnosis. Its regular scanning mainly reflects anatomical morphology and the functional one reflects functional information such as human metabolism and blood flow. T1-weighted and T2-weighted images are most commonly used for highlighting the anatomy and lesions, respectively. Faced with diverse MRI images, doctors often need specialized software to read and understand images, such as Picture Archiving and Communication Systems (PACS) as the large amount information [54] provided by the r
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