Medical image enhancement is a fundamental optimization problem in computer science. Enhanced images can provide better insight of patient's disease and remedy for cure. Researchers have been paying great consideration over this challenging problem yet a significant optimal solution is need of time. This paper presents an integrated approach for medical image enhancement using wavelet transforms and image filtering. We have transformed the RGB image to a gray scale image, the transformed image was then filtered by low pass and high pass band discrete filters of third order. A soft threshold factor applied over decomposed wavelet coefficients obtained in high frequency spectral regions of signal produced a significant noise suppressed signal. Another application of prewitt operator in signal convolution helped in uniform smoothing in one direction and edge detection in other direction. The target smoothed noise suppressed image outperformed for a significant percentage of improvement by bringing significant results over traditional image enhancement techniques. We are ambitious to contrive new wavelet functions based on dyadic de-noising filters and threshold factors (soft and hard thresholds) to suppress the signal noise for robust medical image enhancement as an imminent anticipation.