Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications.  presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with these obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region. The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biekly intervals with lungs at 90% vital capacity. The authors present results by comparing their automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference beten the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75%/spl plusmn/2.29% (mean/spl plusmn/standard deviation).
['Shiying Hu', 'Eric A. Hoffman', 'Joseph M. Reinhardt']
I am writing a research paper on a idea Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images
[{"from": "human", "value": "I am writing a research paper on a idea Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications.  presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with these obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region. The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biekly intervals with lungs at 90% vital capacity. The authors present results by comparing their automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference beten the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75%/spl plusmn/2.29% (mean/spl plusmn/standard deviation)."}]