Medicine Image Enhancement System Based On Rough Sets

With the development of computers, networks and image processing technology, a variety of digital technology gain popularity and application, digital image processing technology as a special tool for processing visual image information, are gradually infiltrate into other field. It has made rapid development while promote the progress of other disciplines extensively. Medical image processing digital is an important branch of image processing; the emergence of digital technology has greatly enhanced the accuracy of medical diagnosis, while reducing costs. Medical image processing technology plays an important role in clinical diagnosis, teaching and research. Medical diagnosis must rely on the analysis of medical images, such as CT, X-ray and B ultrasonic. Some of the image data does not meet standards of clarity cause to diagnosis incorrect. Image enhancement of medical image has great social value.Various low contrast ratio of medical image region of interest and its surrounding structures affect the understanding of the image, so it is the important reason of misdiagnosis. To enhance the imaging contrast, the general methods are to increase the intensity of electromagnetic waves or to extend scanning inspection time; these methods increase the cost of inspection and harm to patients. In this case, the post-processing of medical image is necessary.Medical image enhancement highlights mainly the interest information of the medical image, while weakening or removal of unwanted information, so that useful information is strengthened to facilitate understanding distinction. The purpose of image enhancement, first, using a series of technology to improve visual effects of the image and improve image clarity; second, the image is converted into a form which suit person or machine to interpret and analyze.Rough Set theory is a new mathematical tool dealing with vagueness and uncertainty. It can be used to describe the significance of different attributes and reduce the dimension of knowledge space.Image enhancement algorithm based on rough set theory is proposed combining rough set theory and digital image processing technology and according to a variety of medical imaging principle and the pathological analysis of corresponding disease. This algorithm to describe the medical image as a knowledge system, use indiscernible relation of rough set theory and the knowledge (the density dissimilarities of human organs) of medical image science to define the equivalent relations. Medical image is partitioned into sub-images for background and object, the sub-images of background and object are enhanced respectively and they are combined to form a final enhanced image. In the experiment, regard lung tissue of the chest medical image as the target area, the medical image which gets the outstanding target area enhancement. Plentiful experiments have been carried on medical image, and the contrastive analysis has been carried on the proposed enhancement algorithm with several classical algorithms. And study the distribution separation measurement (DSM), thus has confirmed the proposed enhancement algorithm is more suitable for the medical image, and it can extract contained information of the medical image more effectively than the traditional algorithm.Medical image enhancement system based on rough set is designed, the proposed enhancement algorithm with a range of medical image processing and analysis methods, such as enhancement, segmentation, measurement and medical image processing functions necessary are organized into a system, it can improve the visual quality of medical images, using computer technology and the windows user interface, facilitating the doctor's operation and diagnosis. This tool can achieve a variety of medical images enhancement quickly and effectively, and provide a reliable basis for doctors to accurate diagnosis. The system tried to have friendly interface and is used conveniently, will become a powerful tool for the diagnosis of a doctor daily. It will greatly improve the accuracy and efficiency of diagnostic.