Research On Medical Image Enhancement Based On LBPV In Fractional Differential

With the development of computer science and technology,imaging technology is becoming more and more comprehensive and effective.During this trend of development,imaging equipment is developing rapidly as well.More and more imaging equipment is available for people and owing to this circumstance,medical imaging equipment is used more widely,which enables doctors can diagnose diseases more conveniently using images produced by those equipments.Although doctors can directly diagnose diseases using medical images,the final images may be not suitable enough for the doctor to observe and diagnose because of the various and uncontrollable factors during imaging process.In order to present image information more precisely and provide doctors with clearer and higher-quality images,medical image enhancement has become an indispensable process.In order to perform effective enhancement on medical image,this paper does research on the fractional differential enhancement method and proposes a fractional differential medical image enhancement algorithm based on LBPV by improving the existing problems in the existing method and combining it with LBPV(Local Binary Pattern Variance)theory.The main work of this paper is as follows:(1)The topic of this paper is image enhancement algorithm.Therefore,we introduce the basic principles,advantages and disadvantages of several common image enhancement algorithms.Then we analyze the fractional differential and the local binary algorithm and point out some problems of the traditional fractional differential algorithm.(2)We propose a new fractional differential enhancement algorithm based on local binary pattern variance(LBPV).This algorithm uses LBPV theory to extract features from the image to find the main direction of an image and then constructs a more effective fractional mask according to the main direction of this image.Besides,we also experimentally analyze how to select parameters of the fractional differential operator.Finally,in order to verify the effectiveness of the improved image enhancement algorithm,we apply the fractional differential enhancement algorithm to the Outex dataset and compare our algorithm with three existing fractional differential algorithms.The experiment result shows that our algorithm performs better than the other algorithm on texture images.(3)We also analyze the fracture area in the medical fracture image according to our algorithm’s characteristics.Then we perform image enhancement on medical fracture images using the LBPV-based fractional differential enhancement algorithm.The experiment result shows that the enhanced image by our algorithm can make the fracture area more obvious and more convenient for doctors to observe with their naked eyes.Finally,our algorithm is compared with three existing algorithms.The experiment result shows that our algorithm performs better on enhancing the texture and detail information of medical images than the other algorithms.