The Research And Application Of DR Chest Image Segmentation In The Rexture Retrival

Recent years, digital radiography, especially the application of the DR (direct digital radiography) system leads the traditional radiography into digital field. Comparing with traditional radiography image, direct digital radioghy image has higher quantity and includes more information. After image manipulation, the digital information can be applied in more clinical diagnosis.This paper is based on the clinical application of CAD, and study several key technologies of image processing and analysis in X-ray. The main content includs lung segmentation, rib segmentation and texture-based image retrival techniques.For DR chest images, the precise location of the lungs is the chief mission of a computer-aided diagnosis system to detecte chest disease. This paper proposed two methods to automatically detect the location of the lung boundary points. The first method calculates a set of reference lines to determine the relative position of the lungs in the image. Mediastinal and costal edge points are detected on the image which is the results of the application of a 1-D first derivative Gaussian filter to image rows. In this filtered image, positive values are associated with increasing intensity transitions, while negative values correspond to decreasing variations. Therefore, mediastinal and costal edge points are represented by maxima and minima derivative respectively. The methodology to detect top and bottom boundaries is similar to the one described for lateral border points, except for the use of image columns instead of image rows. To discriminate between lung and clavicle edges, all points locating beyond the exterior part of the costal boundary are eliminated. For improving the detection of lung contours, a method based on the use of active contours models was developed. Vertical and horizontal rectangular regions of interest (ROIs) are studied to identify the preliminary edge. These points are approximations to the lung edges which are adjusted using the active contours models. Interpolation method is used to directly connect the boundary points. The use of the above-mentioned two methods can get better lung border segmentation.Rib region influences the texture analysis of lung area. We need to extracte the rib region before analyzing the texture feature in lung region. Several segmentation methods are presented to detect the ribs in digital chest radiographs, including K—means clustering, Gaussian curve plane threshold methods and hough transform. The evaluations of their results are present in the end of the paper. The experimental results indicate that hough transform method is more effective than the others to detect the ribs in digital chest radiographs. The hough transform is carried out after enhancing the image and thinning the image. Hough transform curve has less effect on the intermittent and has an advantage of curve approximation, so it can get the centerline of the ribs region.Finally, analysis of texture feature extracted by gray level co-occurrence matrix is presented. The rib borders are plotted on the original input image. The difference between rib shadow and lung part without rib shadow is subtracted from the rib shadow since the rib shadow has higher. The recall and precision ratio reveals the retrieval result.