To mitigate the noise effects without information loss at the edges of the radiological images, a well-designed preprocessing algorithm is required to assist the Radiologists. This paper proposes a hybrid adaptive preprocessing algorithm that utilizes a Rudin_Osher_Fatemi (R_O_F) model for edge detection, Richardson_Lucy (R_L) Algorithm for Image Enhancement, and Block Matching 3D Collaborative filtering for denoising of image. The performance of the proposed method is assessed and estimated on two realistic datasets, one on chest X-ray images and the another on MRI/CT images. The proposed hybrid system verifies the data reliability of Gaussian noise affected medical images. The simulation results shows that the proposed adaptive method attains a high value of Peak Signal-to-Noise ratio of 47.4433 dB for chest X-Ray and 46.8674 dB for MRI/CT datasets respectively at a standard deviation value of 2. Performance analysis of the proposed scheme is further carried out using various statistical parameters of Root Mean Square Error, Contrast – to – Noise ratio, Bhattacharya Coefficient and Edge Preservation Index. A comparative analysis on denoised image quality shows that the proposed system achieves better performance than several existing denoising methods.