Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical...Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.展开更多
An efficient voxelization algorithm is presented for polygonal models by using the hardware support for the 2 D rasterization algorithm and the GPU programmable function to satisfy the volumetric display system. The v...An efficient voxelization algorithm is presented for polygonal models by using the hardware support for the 2 D rasterization algorithm and the GPU programmable function to satisfy the volumetric display system. The volume is sampled into slices by the rendering hardware and then slices are rasterated into a series of voxels. A composed buffer is used to record encoded voxels of the target volume to reduce the graphic memory requirement. In the algorithm, dynamic vertexes and index buffers are used to improve the voxelization efficiency. Experimental results show that the algorithm is efficient for a true 3-D display system.展开更多
基金National Key Research and Development Program of China(2022YFC3502302)National Natural Science Foundation of China(82074580)Graduate Research Innovation Program of Jiangsu Province(KYCX23_2078).
文摘Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.
文摘An efficient voxelization algorithm is presented for polygonal models by using the hardware support for the 2 D rasterization algorithm and the GPU programmable function to satisfy the volumetric display system. The volume is sampled into slices by the rendering hardware and then slices are rasterated into a series of voxels. A composed buffer is used to record encoded voxels of the target volume to reduce the graphic memory requirement. In the algorithm, dynamic vertexes and index buffers are used to improve the voxelization efficiency. Experimental results show that the algorithm is efficient for a true 3-D display system.
