ROI of HRCT Enhancement and De-noising Based on Rough Set

Abstract

HRCT (high-resolution computed tomography) image enhancement and de-noising algorithm which is based on rough set was proposed in this paper. The equivalent relations defined by the knowledge (the density dissimilarities of human organs) of medical image science, HRCT image is partitioned into sub-images for background and object. The sub-images of background and object are enhanced and de-noising respectively and they are combined to form a final enhanced image. Lung tissue in the HRCT image of chest is regard as the target area. The HRCT image gets the outstanding target area enhancement. The ROI (Region of Interest) de-noising introduced in this paper can enable doctors freely use the mouse to specify a polygon within the image, this makes possible the introduction of doctor's knowledge and experience into medical image de-noising processing. Plentiful experiments have been carried on HRCT, and the contrastive analysis has been carried on the proposed enhancement and de-noising algorithm with several classical algorithms. A lot of experiments and comparisons are done to study the DSM. Thus has confirmed the proposed enhancement and de-noising algorithm is more suitable for the HRCT.