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Computers, Materials & Continua 2022年第3期70卷 5233-5249页

作者： R.Shijitha P.Karthigaikumar A.Stanly Paul Department of Biomedical Instrumentation Engineering Avinashilingam Institute for Home Science and Higher Education forWomenCoimbatoreIndia Department of ECE Karpagam College of EngineeringCoimbatoreIndia

Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to *** segmentation of brain hemorrhage is crucial,so an enhanced segmentation is carried out in this research *** brain image of various patients has taken using an MRI scanner by the utilization of T1,T2,and FLAIR *** work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet(multires UNet)through morphological *** is hard to precisely segment the brain lesions to extract the existing region of *** crucial step is accomplished by this proposed MMU-Net methodology by precise segmentation of stroke *** proposed method efficiently determines the hemorrhagic stroke with improved accuracy of 95%compared with the existing segmentation techniques such as U-net++,ResNet,Multires UNET and 3D-ResU-Net and also provides improved performance of 2D and 3D U-Net with an enhanced *** performancemeasure of the proposed methodology acquires an improved accuracy,precision ratio,sensitivity,and specificity rate of 0.07%,0.04%,0.04%,and 0.05%in comparison to U-net,ResNet,Multires UNET and 3D-ResU-Net techniques respectively.

关键词： Brain hemorrhage magnetic resonance imaging segmentation multi-resolutional U-Net morphological operations

Hybrid Segmentation Approach for Different Medical Image Modalities

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Computers, Materials & Continua 2022年第11期73卷 3454-3471页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The segmentation process requires separating the image region into sub-regions of similar *** sub-region has a group of pixels having the same characteristics,such as texture or *** paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCM)*** extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation *** existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed *** paper extensively analyzes and summarizes the comparative investigation of these ***,a prediction of the improvement involves the combination of these techniques is *** obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.

关键词： Image segmentation ultrasonic images X-ray images CT images PET images MR images fuzzy c-mean morphological operations active contour

An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

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Big Data Mining and Analytics 2023年第3期6卷 321-335页

作者： Seetharam Khetavath Navalpur Chinnappan Sendhilkumar Pandurangan Mukunthan Selvaganesan Jana Lakshmanan Malliga Subburayalu Gopalakrishnan Sankuru Ravi Chand Yousef Farhaoui Department of Electronics and Communication Engineering Chaitanya(Deemed to be University)Warangal 506001India. Department of Electronics and Communication Engineering Sri Indu College of Engineering&TechnologySherigudaHyderabad 501510India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Telangana 500100India. Department of Electronics and Communication Engineering Nalla Narasimha Reddy Education Society’s Group of Institutions-Integrated CampusHyderabad 500088India. STI Laboratory the IDMS TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 52000Morocco.

The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer ***,sign language recognition is mainly developed for enabling communication between deaf and dumb *** conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture ***,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification ***,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing *** image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture ***,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness ***,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error ***,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition *** results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.

关键词： hand gesture recognition skin color detection morphological operations Multifaceted Feature Extraction(MFE)model Heuristic Manta-ray Foraging Optimization(HMFO) Adaptive Extreme Learning Machine(AELM)

Efficient Segmentation Approach for Different Medical Image Modalities

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Computers, Materials & Continua 2022年第11期73卷 3119-3135页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

This paper presents a study of the segmentation of medical *** paper provides a solid introduction to image enhancement along with image segmentation *** the first step,the morphological operations are employed to ensure image detail protection and *** objective of using morphological operations is to remove the defects in the texture of the ***,the Fuzzy C-Means(FCM)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster *** proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster *** is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition *** results are performed on different medical image ***(Us),X-ray(Mammogram),Computed Tomography(CT),Positron Emission Tomography(PET),and Magnetic Resonance(MR)images are the main medical image modalities used in this *** obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image *** results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,Mammogram,CT,PET,and MRI images,respectively.

关键词： Image segmentation ultrasonic mammogram CT PET MRI morphological operations FCM active contours

A Novel Approach for Brain Tumor Detection Using MRI Images

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Journal of Biomedical Science and Engineering 2016年第10期9卷 44-52页

作者： Abd El Kader Isselmou Shuai Zhang Guizhi Xu Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Department of Biomedical Engineering Hebei University of Technology Tianjin China

Processing magnetic resonance images are very complex and constantly studied by the researchers to give doctors better ability to diagnose the patients. In order to detect automatically suspicious regions or tumors, we present a new approach inspired by threshold segmentation and based on morphological operations in this paper. The advantages of our approach come from the complementarities between these two approaches. The morphological operations extract roughly the tumor region and eventually can affect healthy while the threshold segmentation method gives a clear picture of the structure of the different brain and therefore these two approaches improve significantly the threshold segmentation and detection and extraction of the tumor zone based on morphological operations.

关键词： MRI Threshold Segmentation morphological operations Tumor Identification Filters