Research On Algorithm Of Lung Fields Segmentation In Chest X-ray Radiographs

With the development of economic growth and people’s living standards, lung disease becomes increasingly. In recent years, the incidence of lung cancer is increasing year by year which has become the main factor of the harm and impact the safety of human life. X-ray has the features of small radiation, low price, simple operation, strong usefulness and so on, which is still the preferred method of radiology imaging inspection. Chest X-ray radiograph records the information of health or lesions, it is accounted for40%field of diagnostic imaging of lung disease based on chest X-ray inspection, as a result, X-ray has important medical applications for research value. However, chest X-ray radiograph has many disadvantages and deficiencies, such as:low resolutions, the organizations in images are overlap with each other which leads to almost any organizations in the chest X-ray radiographs do not have clear edge, and the lesions are very easily to be covered up by other organizations, that results in lesions are difficult to distinguish or easily omitted, so it seriously impact on the diagnosis of lung disease. Therefore, computer-aided diagnosis of lung disease has become a major issue in contemporary medical and scientific research.Based on the background of chest X-ray radiograph computer-aided diagnosis, combine with some problems of the chest X-ray radiographs, and research on segmentation of lung fields in chest X-ray radiographs. At the basis of the present lung field segmentation method analysis and summary, combine the characteristics of chest X-ray images and use the improved2d Otsu algorithm, propose a lung field segmentation method which based on combining Otsu and mathematical morphology. First of all, using2d Otsu algorithm obtain image global threshold to implement splitting the original segmentation chest X-ray radiograph; Furthermore, gradually reduce the threshold based on global threshold, and select the optimal threshold value to segment lung fields, as a result of which, to get a more perfect lung field binary image. Finally, by filling up the lung field binary image, smoothing edge and contouring extraction, implement the lung segmentation of chest X-ray radiograph. However, this segmentation of lung field method is complex and the threshold selection is also not accurate, result in the low effectiveness and the low accuracy. Therefore, in order to improve the efficiency and the accuracy of segmentation, in this article, I propose a lung field segmentation method based on genetic algorithm and Otsu algorithm. This method uses improved2d Otsu algorithm as the sufficiency function of genetic algorithm, and at the same time, combine the characteristics of X-ray images to improve the genetic algorithm operation, such as:encoding, selection, crossover, mutation operation and so on, which also fully shows the advantages of genetic algorithm, such as:global search ability, overall optimization strategy and global optimal approximate solution, search the global and local optimal threshold for segmentation of lung field in chest X-ray radiograph. Using global and local optimal threshold to achieve segment lung field and improve the efficiency and accuracy of segmentation.In a conclusion, I select54chest X-ray radiographs from the databases to test segment lung field segmentation, the segmentation method based on combination of genetic algorithm with Otsu could fast and efficient to implement lung field segmentation, and could also get ideal segmentation results. The results showed the lung field segmentation method has high speed, high accuracy, and strong sensitivity. Furthermore, this method is feasible in the practical chest X-ray radiographs in lung field segmentation applications.