Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process.The morphological pattern of an epithelial layer is greatly affected.Theoretical...Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process.The morphological pattern of an epithelial layer is greatly affected.Theoretically,the shape deformation model can normalise the distortions,but it needs a 2D image.Curvatures theory,on the other hand,is not yet tested on digital pathology images.Therefore,this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer.The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line.The epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus only.Then,the textural and spatial features along the estimated layer are used for dysplastic tissue detection.The proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic tissue.The result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth classification.The improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of tissue.Thus,quantifying and classifying the morphological patterns for dysplasia can be automated.The textural and spatial features on the detected epithelial layer can capture the changes in tissue.展开更多
Congestion control is among primary topics in computer network in which random early detection(RED)method is one of its common techniques.Nevertheless,RED suffers from drawbacks in particular when its“average queue l...Congestion control is among primary topics in computer network in which random early detection(RED)method is one of its common techniques.Nevertheless,RED suffers from drawbacks in particular when its“average queue length”is set below the buffer’s“minimum threshold”position which makes the router buffer quickly overflow.To deal with this issue,this paper proposes two discrete-time queue analytical models that aim to utilize an instant queue length parameter as a congestion measure.This assigns mean queue length(mql)and average queueing delay smaller values than those for RED and eventually reduces buffers overflow.A comparison between RED and the proposed analytical models was conducted to identify the model that offers better performance.The proposed models outperform the classic RED in regards to mql and average queueing delay measures when congestion exists.This work also compares one of the proposed models(RED-Linear)with another analytical model named threshold-based linear reduction of arrival rate(TLRAR).The results of the mql,average queueing delay and the probability of packet loss for TLRAR are deteriorated when heavy congestion occurs,whereas,the results of our RED-Linear were not impacted and this shows superiority of our model.展开更多
基金supported by the Center for Research and Innovation Management[CRIM]Universiti Kebangsaan Malaysia[Grant No.FRGS-1-2019-ICT02-UKM02-6]and Ministry of Higher Education Malaysia.
文摘Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process.The morphological pattern of an epithelial layer is greatly affected.Theoretically,the shape deformation model can normalise the distortions,but it needs a 2D image.Curvatures theory,on the other hand,is not yet tested on digital pathology images.Therefore,this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer.The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line.The epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus only.Then,the textural and spatial features along the estimated layer are used for dysplastic tissue detection.The proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic tissue.The result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth classification.The improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of tissue.Thus,quantifying and classifying the morphological patterns for dysplasia can be automated.The textural and spatial features on the detected epithelial layer can capture the changes in tissue.
文摘Congestion control is among primary topics in computer network in which random early detection(RED)method is one of its common techniques.Nevertheless,RED suffers from drawbacks in particular when its“average queue length”is set below the buffer’s“minimum threshold”position which makes the router buffer quickly overflow.To deal with this issue,this paper proposes two discrete-time queue analytical models that aim to utilize an instant queue length parameter as a congestion measure.This assigns mean queue length(mql)and average queueing delay smaller values than those for RED and eventually reduces buffers overflow.A comparison between RED and the proposed analytical models was conducted to identify the model that offers better performance.The proposed models outperform the classic RED in regards to mql and average queueing delay measures when congestion exists.This work also compares one of the proposed models(RED-Linear)with another analytical model named threshold-based linear reduction of arrival rate(TLRAR).The results of the mql,average queueing delay and the probability of packet loss for TLRAR are deteriorated when heavy congestion occurs,whereas,the results of our RED-Linear were not impacted and this shows superiority of our model.
