Method for automated analysis of digital chest radiographs

Abstract

A method for automatically segmenting lung regions in a chest radiographic image comprising; providing an input digital chest radiograph image; preprocessing the input digital radiographic image; extracting the chest body midline and lung centerlines from the preprocessed image. Locating one-by-one, the chest body model, the spine model and the two lung models in the image based on the extracted chest body midline and two lung centerlines; and detecting the lung contours by deforming the lung shape models to converge to the true lung boundaries as a function of the extracting and locating image processing.

1 . A method for processing a radiographic image, comprising: 
 providing the input digital radiographic image;    preprocessing the input digital radiographic image;    extracting features from the preprocessed digital radiographic image; and    processing the input digital radiographic image based on the extracted features.    
     
     
         2 . The method of  claim 1 , wherein the step of preprocessing includes normalizing the input digital radiographic image based on at least one estimated region of interest.  
     
     
         3 . A method for automatically segmenting lung regions in a chest radiographic image comprising: 
 providing an input digital chest radiographic image;    preprocessing the input digital radiographic image by: (a) estimating two lung regions and a mediastinum region, and (b) normalizing the radiographic image with the estimated two lung regions and mediastinum regions;    extracting a chest body midline and two lung centerlines from the preprocessed image;    locating one-by-one, the chest body model, the spine model and the two lung models in the image based on the extracted chest body midline and two lung centerlines; and    detecting the lung regions by deforming the lung shape models to converge to the true lung boundaries using both region and edge information.    
     
     
         4 . The method of  claim 3 , wherein the two lung and mediastinum regions are estimated using appropriate scales and a combination of direction and derivation of a radiographic image.  
     
     
         5 . The method of  claim 3 , wherein the step of normalizing employs a minimal gray-level of the estimated lung regions and a maximal gray-level of the estimated mediastinum region.  
     
     
         6 . The method of  claim 3 , wherein the step of extracting the chest body midline and two lung centerlines uses appropriate scales and a combination of direction and derivation of a radiographic image.  
     
     
         7 . The method of  claim 3 , wherein the chest body midline is detected by searching extreme pixels in the 0 th -order X-direction derivative image in the estimated mediastinum region, and the two lung centerlines are detected by searching the extreme pixels in the 0 th -order X-direction derivative image in the estimated lung regions.  
     
     
         8 . The method of  claim 3 , wherein the chest body model is located by aligning its centerline with the chest body midline, and the chest body model size is derived from the distance between the two lung centerlines.  
     
     
         9 . The method of  claim 3 , wherein the spine model is placed in the middle of the chest body model according to a size and spatial relationship of chest body midline and lung centerlines.  
     
     
         10 . The method of  claim 3 , wherein the two lung models are located using a size and spatial relationship of the detected chest body midline and lung centerlines.  
     
     
         11 . The method of  claim 3 , wherein the two lung models are statistical shape models and the model size and translation parameters are determined from the detected chest body midline and lung centerlines.  
     
     
         12 . The method of  claim 3 , wherein deforming the lung shape models takes advantage of derivative images of an image.