Liver hydatid disease is a common parasitic disease in farm and pastoral areas, which seriously influences people's health. Based on CT imaging features of this disease, an iterative approach for liver segmentatio...Liver hydatid disease is a common parasitic disease in farm and pastoral areas, which seriously influences people's health. Based on CT imaging features of this disease, an iterative approach for liver segmentation and hydatid lesion extraction simultaneously is proposed. In each iteration, our algorithm consists of two main steps: 1) according to the user-defined pixel seeds in the liver and hydatid lesion, Gaussian probability model fitting and smoothed Bayesian classification are applied to get initial segmentation of liver and lesion; 2) the parametric active contour model using priori shape force field is adopted to refine initial segmentation. We make subjective and objective evaluation on the proposed algorithm validity by the experiments of liver and hydatid lesion segmentation on different patients' CT slices. In comparison with ground-truth manual segmentation results, the experimental results show the effectiveness of our method to segment liver and hydatid lesion.展开更多
The current method for inspecting microholes in printed circuit boards(PCBs)involves preparing slices followed by optical microscope measurements.However,this approach suffers from low detection efficiency,poor reliab...The current method for inspecting microholes in printed circuit boards(PCBs)involves preparing slices followed by optical microscope measurements.However,this approach suffers from low detection efficiency,poor reliability,and insufficient measurement stability.Micro-CT enables the observation of the internal structures of the sample without the need for slicing,thereby presenting a promising new method for assessing the quality of microholes in PCBs.This study integrates computer vision technology with computed tomography(CT)to propose a method for detecting microhole wall roughness using a U-Net model and image processing algorithms.This study established an unplated copper PCB CT image dataset and trained an improved U-Net model.Validation of the test set demonstrated that the improved model effectively segmented microholes in the PCB CT images.Subsequently,the roughness of the holes’walls was assessed using a customized image-processing algorithm.Comparative analysis between CT detection based on various edge detection algorithms and slice detection revealed that CT detection employing the Canny algorithm closely approximates slice detection,yielding range and average errors of 2.92 and 1.64μm,respectively.Hence,the detection method proposed in this paper offers a novel approach for nondestructive testing of hole wall roughness in the PCB industry.展开更多
基金Science Special Fund for "Special Training" of Ethnical Minority Professional and Technical Intelligent in Xinjiang sponsored by the Scienceand Technology Department of Xinjiang Uygur Autonomous Regiongrant number:200723104+1 种基金National Natural Science Foundation of Chinagrant number:30960097
文摘Liver hydatid disease is a common parasitic disease in farm and pastoral areas, which seriously influences people's health. Based on CT imaging features of this disease, an iterative approach for liver segmentation and hydatid lesion extraction simultaneously is proposed. In each iteration, our algorithm consists of two main steps: 1) according to the user-defined pixel seeds in the liver and hydatid lesion, Gaussian probability model fitting and smoothed Bayesian classification are applied to get initial segmentation of liver and lesion; 2) the parametric active contour model using priori shape force field is adopted to refine initial segmentation. We make subjective and objective evaluation on the proposed algorithm validity by the experiments of liver and hydatid lesion segmentation on different patients' CT slices. In comparison with ground-truth manual segmentation results, the experimental results show the effectiveness of our method to segment liver and hydatid lesion.
基金Supported by National Natural Science Foundation of China(Grant Nos.52122510 and 52375415).
文摘The current method for inspecting microholes in printed circuit boards(PCBs)involves preparing slices followed by optical microscope measurements.However,this approach suffers from low detection efficiency,poor reliability,and insufficient measurement stability.Micro-CT enables the observation of the internal structures of the sample without the need for slicing,thereby presenting a promising new method for assessing the quality of microholes in PCBs.This study integrates computer vision technology with computed tomography(CT)to propose a method for detecting microhole wall roughness using a U-Net model and image processing algorithms.This study established an unplated copper PCB CT image dataset and trained an improved U-Net model.Validation of the test set demonstrated that the improved model effectively segmented microholes in the PCB CT images.Subsequently,the roughness of the holes’walls was assessed using a customized image-processing algorithm.Comparative analysis between CT detection based on various edge detection algorithms and slice detection revealed that CT detection employing the Canny algorithm closely approximates slice detection,yielding range and average errors of 2.92 and 1.64μm,respectively.Hence,the detection method proposed in this paper offers a novel approach for nondestructive testing of hole wall roughness in the PCB industry.
