<p>Image enhancement is the technique of improving image quality. Medical images are very significant for timely detection and proper treatment of diseases but most medical images suffer from poor contrast, low information and structural content due to noise and excessive light exposure. Human brain is very complex with tiny cells and tissues which result the brain MRI (Magnetic Resonance Imaging) images with weak boundaries, artifacts and varying degrees of noise. Hence, brain MRI image enhancement becomes very important for early detection of disease and accurate treatment. To improve contrast and overall quality, a novel technique named as Fuzzy logic based two stage contrast limited adaptive histogram equalization (FLTSCLAHE) for MRI brain image enhancement is proposed. To overcome the shortcomings of traditional method, a fuzzy logic-based technique is developed, where an input image is subjected to fuzzy inference system (FIS) which gives fuzzy enhanced image. Fuzzy enhanced image is further processed in second stage using two stage contrast limited adaptive histogram equalization (CLAHE). The output image of stage two is rich in contrast but have low structural information. To address this, in third stage the image is further processed using Laplacian filter to get the fine details of brain MRI images. The output image is rich in contrast and structural information along with that it overcomes the drawback of over and under enhancement which is generally observed in traditional image enhancement techniques. The proposed technique is tested using brain MRI images available in open data base and comparison is done with several other existing image enhancement techniques. Results show the efficacy of FLTSCLAHE technique over other existing methods.</p>