Radiographic X-ray Images Enhancement with Edge Preservation using Singular Value Decomposition

Abstract

Contrast enhancement is one of the important issues in Medical X-ray imaging since these image, in general, are of low contrast and luminance. In medical X-ray imaging system viewing the bone structure and soft tissues are important for better medical diagnosis. The accuracy of Medical diagnosis of a patient purely depends on the clarity of the image. Hence an X-ray image must be well enhanced at the same time edges must be preserved and highlighted while applying image pre-processing technique. This is a challenging task in literature. In literature many techniques had been proposed for improving the low contrast images in various applications like satellite images, medical images, etc. Standard methods include General Histogram Equalization (GHE), Local Histogram Equalization (LHE), AHE or CLACHE, Brightness Preserving Histogram Equalization (BBHE), etc. All these methods rely on histogram equalization on the entire image, might lead to loss of edge information. Since Soft-Tissues and bone pixels have similar values, global equalization methods might fail. So to resolve these challenges, this paper presents a new method using Singular Value Decomposition (SVD) for image enhancement and also improves the edge quality. Proposed method works in two phases: background suppression and foreground enhancement. The proposed method decomposes the x-ray image using SVD and extracts the singular values of the image (which represents the order of luminance in the image). These singular values are further analyzed to identify the highly dominating singular values and are used for background suppression. Later the foregrounds, i.e., the bone pixels are enhanced through histogram equalization. Advantage of the proposed method is shown experimentally using various images like a hand, pelvic, skull and chest of a human. As standard matrices, PSNR, SNR, and Entropy focus on complete enhanced image (i.e., foreground and background) might fail to justify the improvement in enhancement. Thus, in this paper performance is evaluated using standard texture metrics: homogeneity, contrast, entropy, mean and standard deviation. Results of the proposed method are compared with standard literature methods like AHE, CLACHE, MMBEBH, and BHE. The proposed method has shown the better results with highest homogeneity (0.88), lowest contrast (0.32), highest correlation (0.97), and highest energy (0.21). Edge preservation accuracy is also highest (i.e., 0.98%) in comparison to literature methods.