Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PC...Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.展开更多
Carbon neutralization has been introduced as a long-term policy to control global warming and climate change.As plant photosynthesis produces the most abundant lignocellulosic biomass on Earth,its conversion to biofue...Carbon neutralization has been introduced as a long-term policy to control global warming and climate change.As plant photosynthesis produces the most abundant lignocellulosic biomass on Earth,its conversion to biofuels and bioproducts is considered a promising solution for reducing the net carbon release.However,natural lignocellulose recalcitrance crucially results in a costly biomass process along with secondary waste liberation.By updating recent advances in plant biotechnology,biomass engineering,and carbon nanotechnology,this study proposes a novel strategy that integrates the genetic engineering of bioenergy crops with green-like biomass processing for cost-effective biofuel conversion and high-value bioproduction.By selecting key genes and appropriate genetic manipulation approaches for precise lignocellulose modification,this study highlights the desirable genetic site mutants and transgenic lines that are raised in amorphous regions and inner broken chains account for high-density/length-reduced cellulose nanofiber assembly in situ.Since the amorphous regions and inner-broken chains of lignocellulose substrates are defined as the initial breakpoints for enhancing biochemical,chemical,and thermochemical conversions,desirable cellulose nanofibers can be employed to achieve nearcomplete biomass enzymatic saccharification for maximizing biofuels or high-quality biomaterials,even under cost-effective and green-like biomass processes in vitro.This study emphasizes the optimal thermal conversion for generating high-performance nanocarbons by combining appropriate nanomaterials generated from diverse lignocellulose resources.Therefore,this study provides a perspective on the potential of green carbon productivity as a part of the fourth industrial revolution.展开更多
基金This research is supported by the National Key Research and Development Program of China(2018YFB0804202,2018YFB0804203)Regional Joint Fund of NSFC(U19A2057),the National Natural Science Foundation of China(61672259,61876070)and the Jilin Province Science and Technology Development Plan Project(20190303134SF,20180201064SF).
文摘Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.
基金supported by the National Natural Science Foundation of China(32170268 to L.P)the National 111 Project of the Ministry of Education of China(BP0820035 to L.P,D17009 to J.T)+1 种基金the Initiative Grant of Hubei University of Technology for High-level Talents(GCC20230001 to L.P)the Shandong Energy Institute,China(SEI I202142 to C.F).
文摘Carbon neutralization has been introduced as a long-term policy to control global warming and climate change.As plant photosynthesis produces the most abundant lignocellulosic biomass on Earth,its conversion to biofuels and bioproducts is considered a promising solution for reducing the net carbon release.However,natural lignocellulose recalcitrance crucially results in a costly biomass process along with secondary waste liberation.By updating recent advances in plant biotechnology,biomass engineering,and carbon nanotechnology,this study proposes a novel strategy that integrates the genetic engineering of bioenergy crops with green-like biomass processing for cost-effective biofuel conversion and high-value bioproduction.By selecting key genes and appropriate genetic manipulation approaches for precise lignocellulose modification,this study highlights the desirable genetic site mutants and transgenic lines that are raised in amorphous regions and inner broken chains account for high-density/length-reduced cellulose nanofiber assembly in situ.Since the amorphous regions and inner-broken chains of lignocellulose substrates are defined as the initial breakpoints for enhancing biochemical,chemical,and thermochemical conversions,desirable cellulose nanofibers can be employed to achieve nearcomplete biomass enzymatic saccharification for maximizing biofuels or high-quality biomaterials,even under cost-effective and green-like biomass processes in vitro.This study emphasizes the optimal thermal conversion for generating high-performance nanocarbons by combining appropriate nanomaterials generated from diverse lignocellulose resources.Therefore,this study provides a perspective on the potential of green carbon productivity as a part of the fourth industrial revolution.
