Abstract
Image enhancement technology is often used to improve the quality of medical images and helps doctors or expert systems identify and diagnose diseases. This paper aimed at the characteristics of magnetic resonance imaging (MRI) with complex and difficult-to-enhance details and to propose a nonsubsampled contourlet transform- (NSCT-) based enhancement algorithm called MIE-NSCT. NSCT was used for MRI sub-band decomposition. For high-pass sub-bands, four fuzzy rules were proposed to enhance multiscale and multidirectional edge contour details from adjacent eight directions, whilst for low-pass sub-bands, a new adaptive histogram enhancement algorithm was proposed. The problem of noise amplification and loss of details during the enhancement process was solved. The algorithm was verified on the public dataset BraTS2017 and compared with other advanced methods. Experimental results showed that MIE-NSCT had obvious advantages in improving the quality of medical images, and high-quality medical images showed enhanced performance in grading tumour. MIE-NSCT is suitable for integration into an interactive expert system to provide support for the visualization of disease diagnosis.
Highlights
Medical images could visually and noninvasively describe the structure of the human body
Image Enhancement. ree image enhancement methods, namely, CLAHE [21], dual-tree complex wavelet transform (DTCWT) [27], and feedback adaptively weighted dense network (FAWDN) [30], were compared with MIE-nonsubsampled contourlet transform (NSCT) to verify the results of image enhancement
For low-pass sub-bands, global and local structure adaptive histogram equalization technology was improved to divide the images into intersegment and intrasegment areas
Summary
Medical images could visually and noninvasively describe the structure of the human body. With the continuous development of medical imaging technology, the reliance of disease detection and diagnosis on the information provided by medical images continues to increase [1]. Experienced radiologists and clinicians could obtain useful information, such as the shape and texture of certain tissue organs, from medical images to diagnose and identify complex diseases. MRI could provide a wealth of physiological tissue information because of its great advantages in soft-tissue imaging. The original MRI is usually affected by factors, such as equipment and acquisition conditions in the imaging process, resulting in image quality degradation [2]. Artifacts, and low contrast are the main problems of MRI. Low-quality medical imaging could affect the postprocessing of images and the diagnosis by doctors [3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
