Reducing Noise and Improving Image Contrast with a Hybrid Approach to Enhance the Quality of Medical Imaging

Medical image enhancement is one of the most critical areas in modern medicine, as it helps in disease diagnosis and medical action on the basis of diagnostic accuracy. This paper proposes a new hybrid model having new techniques of noise processing and contrast enhancement for medical images.

An adaptive Wiener filter can be used to remove noise, the contrast-limited adaptive histogram equalization (CLAHE) algorithm can be used to enhance picture contrast, gamma transforms to correct for illumination, and wavelet transforms may also be employed as another means in order to amplify fine details. For this purpose, the method was first implemented on the retinal images. After that, the same method was long computed tomography (CT) images and finally magnetic resonance imaging (MRI) pictures. To reflect the fractal nature of medical images, 10 retinal images were taken; 10 CT pictures and 10 MRI scans, as a statistical sampling to provide more accurate weighing of the effectiveness in image processing technologies with respect to different medical imaging modalities.

In extreme contrast conditions, by contrasting the results yielded by the proposed method with those of some existing methods, for example, conventional histogram equalization (HE) and CLAHE, considerable enhancement in the image quality could be obtained. The results indicated that our proposed approach outperforms the former approach by suppressing noise but preserving the microstructures of tissues or anatomical structures. It also improves the contrast evenly, providing increased sharpness of the images without loss of vital information and distortion.

This suggested technique equates the value priorities with additional harmonious; this suggests the image is shrewdly for medical purpose. In addition, this allows for translation and better ability to identify more subtlety and specificity tissue, the essence of any medical diagnosis.