Delineation of the lung parenchyma in the thoracic Computed Tomography(CT)is an important processing step for most of the pulmonary image analysis such as lung volume extraction,lung nodule detection and pulmonary ves...Delineation of the lung parenchyma in the thoracic Computed Tomography(CT)is an important processing step for most of the pulmonary image analysis such as lung volume extraction,lung nodule detection and pulmonary vessel segmentation.An automatic method for accurate delineation of lung parenchyma in thoracic Computed Tomography images is presented in this paper.The proposed method involves a segmentation phase followed by a lung boundary correction technique.The tissues in the thoracic Computed Tomography can be represented by a number of Gaussians.We propose a histogram utilized Adaptive Multilevel Thresholding(AMT)for estimating the total number of Gaussians and their initial parameters.The parameters of Gaussian components are updated by Expectation Maximization(EM)algorithm.The segmented lung parenchyma from the Gaussian Mixture model(GMM)undergoes an Adaptive Morphological Filtering(AMF)to reduce the boundary errors.The proposed method has been tested on 70 diseased and 119 normal lung images from 28 cases obtained from Lung Image Database Consortium(LIDC).The performance of the proposed system has been validated.展开更多
Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software.The existing research applies various optimization methods such as Genetic Algorithm,Cr...Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software.The existing research applies various optimization methods such as Genetic Algorithm,Crow Search Algorithm,Ant Colony Optimization,etc.,for test case optimization.The existing methods have limitations of lower efficiency in fault diagnosis,higher computa-tional time,and high memory requirement.The existing methods have lower effi-ciency in software test case optimization when the number of test cases is high.This research proposes the Tournament Winner Genetic Algorithm(TW-GA)method to improve the efficiency of software test case optimization.Hospital Information System(HIS)software was used to evaluate TW-GA model perfor-mance in test case optimization.The tournament Winner in the proposed method selects the instances with the best fitness values and increases the exploitation of the search to find the optimal solution.The TW-GA method has higher exploita-tion that helps to find the mutant and equivalent mutation that significantly increases fault diagnosis in the software.The TW-GA method discards the infor-mation with a lower fitness value that reduces the computational time and mem-ory requirement.The TW-GA method requires 5.47 s and the MOCSFO method requires 30 s for software test case optimization.展开更多
文摘Delineation of the lung parenchyma in the thoracic Computed Tomography(CT)is an important processing step for most of the pulmonary image analysis such as lung volume extraction,lung nodule detection and pulmonary vessel segmentation.An automatic method for accurate delineation of lung parenchyma in thoracic Computed Tomography images is presented in this paper.The proposed method involves a segmentation phase followed by a lung boundary correction technique.The tissues in the thoracic Computed Tomography can be represented by a number of Gaussians.We propose a histogram utilized Adaptive Multilevel Thresholding(AMT)for estimating the total number of Gaussians and their initial parameters.The parameters of Gaussian components are updated by Expectation Maximization(EM)algorithm.The segmented lung parenchyma from the Gaussian Mixture model(GMM)undergoes an Adaptive Morphological Filtering(AMF)to reduce the boundary errors.The proposed method has been tested on 70 diseased and 119 normal lung images from 28 cases obtained from Lung Image Database Consortium(LIDC).The performance of the proposed system has been validated.
文摘Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software.The existing research applies various optimization methods such as Genetic Algorithm,Crow Search Algorithm,Ant Colony Optimization,etc.,for test case optimization.The existing methods have limitations of lower efficiency in fault diagnosis,higher computa-tional time,and high memory requirement.The existing methods have lower effi-ciency in software test case optimization when the number of test cases is high.This research proposes the Tournament Winner Genetic Algorithm(TW-GA)method to improve the efficiency of software test case optimization.Hospital Information System(HIS)software was used to evaluate TW-GA model perfor-mance in test case optimization.The tournament Winner in the proposed method selects the instances with the best fitness values and increases the exploitation of the search to find the optimal solution.The TW-GA method has higher exploita-tion that helps to find the mutant and equivalent mutation that significantly increases fault diagnosis in the software.The TW-GA method discards the infor-mation with a lower fitness value that reduces the computational time and mem-ory requirement.The TW-GA method requires 5.47 s and the MOCSFO method requires 30 s for software test case optimization.
