Image Stitching of the Computed Radiology images Using a Pixel-Based Approach

In this paper, a method for automatic stitching of radiology images based on pixel features has been presented. In this method, according to the smooth texture of radiological images and in order to increase the number of the extracted features after quality enhancement of initial radiology images, 45 degree isotropic mask is applied to each radiology image to observe the image details. After this process, we used statistical and heuristic image noise extraction method (SHINE) to acceptably reduce the noise resulting from radiation of alternating X-rays on detector. Pixel point’s features are obtained by selecting maximum or minimum value of the brightness of pixels in certain neighborhood of the resulting radiology images. This algorithm transmutes point’s features to 128 dimensional vector features. In order to identify the segments overlapping in basic radiology images, we specify equivalent vector features of each radiology image using the mathematical properties of the vectors and find the fit geometry transform between pairs features matched by the random sample consensus (RANSAC) algorithm. Finally, resulted motion model is applied to the initial radiology images and we stitch them together in a common surface