Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)images.The segmentation of hepatic organ is more intricate task,owing to the fact that it posses...Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)images.The segmentation of hepatic organ is more intricate task,owing to the fact that it possesses a sizeable quantum of vascularization.This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans.The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not.This involves segmentation of the region of interest(ROI)from the segmented liver,extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features.In this work,the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering(FCM)which is one of the algorithms to segment the images.The dataset used in this method has been collected from various repositories and scan centers.The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency.It gives better results when compared with other existing algorithms.Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.展开更多
Sugarcane mechanized planting technology consists of seed preparation and field planting.This study aims at the issues of easy damage to the seeds during the operation of the automatic cutting machine for single-bud s...Sugarcane mechanized planting technology consists of seed preparation and field planting.This study aims at the issues of easy damage to the seeds during the operation of the automatic cutting machine for single-bud segment sugarcane,lack of intelligent seed selection and calibration technology,low recognition accuracy,and the need for manual feeding of the planting machine’s seed meter which leads to seed leakage.This study,based on machine vision and deep learning,optimizes the seed calibration method and proposes an improved YoloV5-STD target detection algorithm to improve the recognition accuracy of seed characteristics and optimize the overall engineering structure.For the planting machine,a new type of hopper for the seed meter is designed using natural rubber as the base material mixed with polystyrene,and the flexible automatic seed metering mechanism is analyzed to achieve automatic feeding and seed metering.Test assessment indicators were formulated based on the enterprise standards of the Institute of Agricultural Machinery Research,Chinese Academy of Tropical Agricultural Sciences.Experimental results show that the recognition accuracy of the 2DZ-2 type single-bud segment intelligent cutting machine is≥95%,the bud injury rate is<1.8%,the qualified rate of cutting is 95.8%,and the single-channel cutting efficiency is 64 buds/min.The 2CZD-2C type single-bud segment planter has a planting qualification rate of 96.6%,a planting efficiency of 208 buds/min,and a seed leakage rate of<2.1%.展开更多
文摘Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)images.The segmentation of hepatic organ is more intricate task,owing to the fact that it possesses a sizeable quantum of vascularization.This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans.The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not.This involves segmentation of the region of interest(ROI)from the segmented liver,extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features.In this work,the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering(FCM)which is one of the algorithms to segment the images.The dataset used in this method has been collected from various repositories and scan centers.The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency.It gives better results when compared with other existing algorithms.Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.
基金supported by the Project of Guangxi Zhuang Autonomous Region Key Technologies R&D Program(Grant No.GK AB23026069 and GN AB241484034)the Basic Scientific Research Expenses of Central Public Welfare Scientific Research Institutes(Grant No.1630132022001)+1 种基金the Basic Scientific Research Expenses of Chinese Academy of Tropical Agricultural Sciences(Grant No.1630132024004)the Zhanjiang Science and Technology Project(Grant No.2023A01009).
文摘Sugarcane mechanized planting technology consists of seed preparation and field planting.This study aims at the issues of easy damage to the seeds during the operation of the automatic cutting machine for single-bud segment sugarcane,lack of intelligent seed selection and calibration technology,low recognition accuracy,and the need for manual feeding of the planting machine’s seed meter which leads to seed leakage.This study,based on machine vision and deep learning,optimizes the seed calibration method and proposes an improved YoloV5-STD target detection algorithm to improve the recognition accuracy of seed characteristics and optimize the overall engineering structure.For the planting machine,a new type of hopper for the seed meter is designed using natural rubber as the base material mixed with polystyrene,and the flexible automatic seed metering mechanism is analyzed to achieve automatic feeding and seed metering.Test assessment indicators were formulated based on the enterprise standards of the Institute of Agricultural Machinery Research,Chinese Academy of Tropical Agricultural Sciences.Experimental results show that the recognition accuracy of the 2DZ-2 type single-bud segment intelligent cutting machine is≥95%,the bud injury rate is<1.8%,the qualified rate of cutting is 95.8%,and the single-channel cutting efficiency is 64 buds/min.The 2CZD-2C type single-bud segment planter has a planting qualification rate of 96.6%,a planting efficiency of 208 buds/min,and a seed leakage rate of<2.1%.
