In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel ...In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.展开更多
结构磁共振成像(s MRI)本质上具有三维张量结构,而传统的向量空间机器学习方法将其展开成向量进行建模,这破坏了数据的内在结构信息的完整性,降低了机器学习性能。为了克服数据向量化的弊端,提出了一种基于支持张量机(Support tensor ma...结构磁共振成像(s MRI)本质上具有三维张量结构,而传统的向量空间机器学习方法将其展开成向量进行建模,这破坏了数据的内在结构信息的完整性,降低了机器学习性能。为了克服数据向量化的弊端,提出了一种基于支持张量机(Support tensor machine,STM)的以3D T1加权MR脑白质图像为输入的阿尔兹海默症诊断算法。首先用SPM8软件将采集的MRI数据进行预处理,分割为灰质、白质、脑脊液3部分,提取脑白质各体素的灰度值构建三阶灰度张量,然后用递归特征消除(Recursive Feature Elimination,RFE)法结合支持张量机进行特征选择,最后用支持张量机进行分类。在阿尔兹海默症患者(AD),轻度认知障碍患者(MCI)(包括转化为AD的MCI-C和未转化的MCI-NC)以及正常对照(NC)4组人群中进行实验测试,并用10折交叉验证方法获得验证结果。用ROC曲线下面积AUC、分类准确率、敏感性、特异性这4个指标评价分类器的性能,AD vs NC组分别达到99.1%、97.14%、95.71%、98.57%;AD vs MCI组分别达到88.29%、84.07%、78.57%、91.07%;MCI vs NC组分别达到89.18%、87.91%、93.75%、78.57%;MCI-C vs MCI-NC组分别达到87.5%、82.08%、80.36%、82.14%。算法保持了原始图像的张量结构,提高了分类器的性能,实验结果表明此算法是一种有效的阿尔兹海默症诊断方法。展开更多
文摘In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.
文摘结构磁共振成像(s MRI)本质上具有三维张量结构,而传统的向量空间机器学习方法将其展开成向量进行建模,这破坏了数据的内在结构信息的完整性,降低了机器学习性能。为了克服数据向量化的弊端,提出了一种基于支持张量机(Support tensor machine,STM)的以3D T1加权MR脑白质图像为输入的阿尔兹海默症诊断算法。首先用SPM8软件将采集的MRI数据进行预处理,分割为灰质、白质、脑脊液3部分,提取脑白质各体素的灰度值构建三阶灰度张量,然后用递归特征消除(Recursive Feature Elimination,RFE)法结合支持张量机进行特征选择,最后用支持张量机进行分类。在阿尔兹海默症患者(AD),轻度认知障碍患者(MCI)(包括转化为AD的MCI-C和未转化的MCI-NC)以及正常对照(NC)4组人群中进行实验测试,并用10折交叉验证方法获得验证结果。用ROC曲线下面积AUC、分类准确率、敏感性、特异性这4个指标评价分类器的性能,AD vs NC组分别达到99.1%、97.14%、95.71%、98.57%;AD vs MCI组分别达到88.29%、84.07%、78.57%、91.07%;MCI vs NC组分别达到89.18%、87.91%、93.75%、78.57%;MCI-C vs MCI-NC组分别达到87.5%、82.08%、80.36%、82.14%。算法保持了原始图像的张量结构,提高了分类器的性能,实验结果表明此算法是一种有效的阿尔兹海默症诊断方法。
