Computed radiography skull image enhancement using Wiener filter

Medical imaging devices are used to scan different organs of human being and used in different stages of analysis. Magnetic Resonance Image (MRI), Computer Tomography (CT), Ultrasound and X-Ray are some of the imaging techniques adopted for acquiring images to diagnose most of the diseases. The main aim of this study is to improve the quality of Computed Radiography (CR) medical images. Denoising with edge preservation is very important in CR X-Ray imaging. Noise reduction should be a great concern in order not to lose detailed spatial information for perfect and optimal diagnosis of diseases. Computing techniques also need to be taken care of since the digital format of the medical images is comprised with large sized matrices. In this study, firstly, we compared a series of filtering techniques using Wiener filtering method to remove the Poisson noise from CR X-Ray human Skull images. Secondly, Contrast Enhancement was performed by using Histogram Equalization and intensity value adjustment with limits points. The main aim of this work is to improve the visual quality of CR X-Ray human skull images and enhance the subtle details such as edges and nodules, which are with low contrast white circular objects. The performance of the proposed method is analyzed using Means Square Error (MSE) and Peak Signal Noise Ratio (PSNR) measures. Experimental results show that Wiener Filtering method effectively reduce the Poisson noise from CR X-Ray of a human Skull image. Finally the study is concluded with future implications for research areas.