Contrast enhancement of medical images using fuzzy set theory and nonsubsampled shearlet transform

Abstract

Noises and artifacts are introduced in medical images during the process of imaging and transmission, resulting in reduced definition and lack of detail. Therefore, a contrast enhancement method, based on fuzzy set theory and nonsubsampled shearlet transform (NSST), is proposed. First, the original image is decomposed into several high‐frequency components and a low‐frequency component by NSST. Then, the threshold method is used to remove noises in the high‐frequency components. In addition, a linear stretch is used to improve the overall contrast in the low‐frequency component. Then, the reconstruct image is reconstructed by applying the inverse NSST to the processed high‐frequency and low‐frequency components. Finally, the fuzzy contrast is used to improve the detail information and global contrast in the reconstruct image. Experimental results indicate that, relative to contrast algorithms, the peak signal‐to‐noise ratio of the proposed method is improved by approximately 18%, and the root mean square error (RMSE) is optimized to approximately 48%. The proposed method also improves the image definition and texture information. Moreover, when compared with the Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement, the processing time (time) of this proposed method optimizes about 86%, which can obviously improve the computational efficiency of this method.

A Comparative Study on X-Ray image Enhancement Based on Neutrosophic Set

“…FS theory is widely applied in many fields, one of which is medical image enhancement and denoising. Qingrong et al [3] Proposed a contrast-enhancement technique for medical images that makes use of FS theory and the nonsubsampled shearlet transform (NSST) to enhance the detail and definition of the image. Another proposed by Ahmed Fadil et al [4] The fuzzy c-means clustering (FCM) technique is used to enhance medical images.…”