In the digestion of amino acids,carbohydrates,and lipids,as well as protein synthesis from the consumed food,the liver has many diverse responsibilities and functions that are to be performed.Liver disease may impact ...In the digestion of amino acids,carbohydrates,and lipids,as well as protein synthesis from the consumed food,the liver has many diverse responsibilities and functions that are to be performed.Liver disease may impact the hormonal and nutritional balance in the human body.The earlier diagnosis of such critical conditions may help to treat the patient effectively.A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver.The proposed approach can recognize the abnormalities with better accuracy without training,unlike in supervisory procedures requiring considerable computational efforts for training.In the earlier stages,the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation.Then,the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted—Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information,which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts.It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%.The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.展开更多
Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins ar...Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins are limited,especially in automation,heavily dependent on large amounts of data and resources,lacking the flexibility to adapt to different scenarios.To address these challenges,this paper introduces a novel image segmentation model,CableSAM,specifically designed for automated segmentation of cabin cables.CableSAM improves segmentation efficiency and accuracy using knowledge distillation and employs a context ensemble strategy.It accurately segments cables in various scenarios with minimal input prompts.Comparative experiments on three cable datasets demonstrate that CableSAM surpasses other advanced cable segmentation methods in performance.展开更多
Pulse-coupled neural network （PCNN） is a novel neural network, which has been widely used in image segmentation. However, there are still some limitations, such as trial-and-error parameter settings and manual selec...Pulse-coupled neural network （PCNN） is a novel neural network, which has been widely used in image segmentation. However, there are still some limitations, such as trial-and-error parameter settings and manual selection of the optimal results. This paper puts forward a new method based on the simplified PCNN model for automatic image segmentation. By calculating the un- iformity measure of the corresponding image at each process of iteration, the optimal segmentation result is obtained when the max- imum value of the uniformity measure is achieved. Experimental results show that the proposed method can automatically achieve better segmentation result and has a common adaptability.展开更多
Histomorphometric analysis of histologic sections of normal and diseased bone samples,such as healing allografts and fractures,is widely used in bone research.However,the utility of traditional semi-automated methods ...Histomorphometric analysis of histologic sections of normal and diseased bone samples,such as healing allografts and fractures,is widely used in bone research.However,the utility of traditional semi-automated methods is limited because they are labor-intensive and can have high interobserver variability depending upon the parameters being assessed,and primary data cannot be re-analyzed automatically.Automated histomorphometry has long been recognized as a solution for these issues,and recently has become more feasible with the development of digital whole slide imaging and computerized image analysis systems that can interact with digital slides.Here,we describe the development and validation of an automated application（algorithm）using Visiopharm＇s image analysis system to quantify newly formed bone,cartilage,and fibrous tissue in healing murine femoral allografts in high-quality digital images of H＆E/alcian blue-stained decalcified histologic sections.To validate this algorithm,we compared the results obtained independently using OsteoMeasureTM and Visiopharm image analysis systems.The intraclass correlation coefficient between Visiopharm and OsteoMeasure was very close to one for all tissue elements tested,indicating nearly perfect reproducibility across methods.This new algorithm represents an accurate and labor-efficient method to quantify bone,cartilage,and fibrous tissue in healing mouse allografts.展开更多
Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills.Several attempts are reported in the past for assessment of chronological age of an individual base...Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills.Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images.The permutation and combination of these features realized satisfactory accuracies for a set of limited groups.In this paper,assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images.A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process.Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image.A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female.The experimentations are conducted on the datasets of Radiological Society of North America(RSNA)of about 12442 images.Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%.Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%.展开更多
基金The authors have not received any specific funding for this study.This pursuit is a part of their scholarly endeavors.
文摘In the digestion of amino acids,carbohydrates,and lipids,as well as protein synthesis from the consumed food,the liver has many diverse responsibilities and functions that are to be performed.Liver disease may impact the hormonal and nutritional balance in the human body.The earlier diagnosis of such critical conditions may help to treat the patient effectively.A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver.The proposed approach can recognize the abnormalities with better accuracy without training,unlike in supervisory procedures requiring considerable computational efforts for training.In the earlier stages,the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation.Then,the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted—Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information,which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts.It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%.The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.
基金supported by the Innovation Foundation of National Commercial Aircraft Manufacturing Engineering Technology Research Center(No.COMAC-SFGS-2022-1877)in part by the National Natural Science Foundation of China(No.92048301)。
文摘Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins are limited,especially in automation,heavily dependent on large amounts of data and resources,lacking the flexibility to adapt to different scenarios.To address these challenges,this paper introduces a novel image segmentation model,CableSAM,specifically designed for automated segmentation of cabin cables.CableSAM improves segmentation efficiency and accuracy using knowledge distillation and employs a context ensemble strategy.It accurately segments cables in various scenarios with minimal input prompts.Comparative experiments on three cable datasets demonstrate that CableSAM surpasses other advanced cable segmentation methods in performance.
文摘Pulse-coupled neural network （PCNN） is a novel neural network, which has been widely used in image segmentation. However, there are still some limitations, such as trial-and-error parameter settings and manual selection of the optimal results. This paper puts forward a new method based on the simplified PCNN model for automatic image segmentation. By calculating the un- iformity measure of the corresponding image at each process of iteration, the optimal segmentation result is obtained when the max- imum value of the uniformity measure is achieved. Experimental results show that the proposed method can automatically achieve better segmentation result and has a common adaptability.
基金funded by grants(1S10RR027340-01 and AR43510) to BFB,and (R01 DE019902,P30 AR061307 and P50 AR054041) to EMS,from the National Institutes of Health
文摘Histomorphometric analysis of histologic sections of normal and diseased bone samples,such as healing allografts and fractures,is widely used in bone research.However,the utility of traditional semi-automated methods is limited because they are labor-intensive and can have high interobserver variability depending upon the parameters being assessed,and primary data cannot be re-analyzed automatically.Automated histomorphometry has long been recognized as a solution for these issues,and recently has become more feasible with the development of digital whole slide imaging and computerized image analysis systems that can interact with digital slides.Here,we describe the development and validation of an automated application（algorithm）using Visiopharm＇s image analysis system to quantify newly formed bone,cartilage,and fibrous tissue in healing murine femoral allografts in high-quality digital images of H＆E/alcian blue-stained decalcified histologic sections.To validate this algorithm,we compared the results obtained independently using OsteoMeasureTM and Visiopharm image analysis systems.The intraclass correlation coefficient between Visiopharm and OsteoMeasure was very close to one for all tissue elements tested,indicating nearly perfect reproducibility across methods.This new algorithm represents an accurate and labor-efficient method to quantify bone,cartilage,and fibrous tissue in healing mouse allografts.
文摘Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills.Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images.The permutation and combination of these features realized satisfactory accuracies for a set of limited groups.In this paper,assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images.A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process.Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image.A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female.The experimentations are conducted on the datasets of Radiological Society of North America(RSNA)of about 12442 images.Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%.Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%.
