Review of computer engineering research最新文献

Cyber attacks on UAV networks: A comprehensive survey 对无人机网络的网络攻击：全面调查

Review of computer engineering research Pub Date : 2024-02-09 DOI: 10.18488/76.v11i1.3636

Ashish Mahalle, Sarika Khandelwal, Abhishek Dhore, Vishwajit Barbudhe, Vivek Waghmare

{"title":"Cyber attacks on UAV networks: A comprehensive survey","authors":"Ashish Mahalle, Sarika Khandelwal, Abhishek Dhore, Vishwajit Barbudhe, Vivek Waghmare","doi":"10.18488/76.v11i1.3636","DOIUrl":"https://doi.org/10.18488/76.v11i1.3636","url":null,"abstract":"New technologies are constantly emerging in the modern world and changing the way we live our everyday lives. Although technology has many useful applications, there are various ways it can potentially be abused. Unmanned aerial vehicles (UAVs) are one of the most rapidly developing technologies, with potentially far-reaching consequences. A new focus on UAV applications has fueled rising concerns with regards to security, specifically around networked UAVs. UAVs may be managed from a remote place with relative ease. Essential operations involving the use of military tactics and weapons involve employing them in a variety of situations, such as reconnaissance, surveillance, and offensive, defensive, and civilian capacities. Message insertion, message manipulation, jamming, and GPS spoofing are the most commonly used cyber-attacks against these systems. Ensuring the security of electronics and communications in systems that employ several UAVs is of utmost importance to guarantee their safety and dependability in military and civilian activities. Many technological methods have been developed over the past decade for securing UAVs from cyber-attacks. This paper attempts to summaries the problems that can arise with unmanned aerial vehicles (UAVs), cyber-attacks, and the countermeasures used to protect against them. This is the first paper that details all of the past cyber-attacks on unmanned aerial vehicles (UAVs).","PeriodicalId":493889,"journal":{"name":"Review of computer engineering research","volume":"227 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing skin lesion segmentation with U-Net++: Design, analysis, and performance evaluation 利用 U-Net++ 增强皮损分割：设计、分析和性能评估

Review of computer engineering research Pub Date : 2024-02-09 DOI: 10.18488/76.v11i1.3635

S. Patil, Hitendra D. Patil

{"title":"Enhancing skin lesion segmentation with U-Net++: Design, analysis, and performance evaluation","authors":"S. Patil, Hitendra D. Patil","doi":"10.18488/76.v11i1.3635","DOIUrl":"https://doi.org/10.18488/76.v11i1.3635","url":null,"abstract":"The present research examines the enhancement of skin lesion segmentation with U-Net++. Achieving accurate classification of dermoscopy images is heavily contingent on the precise segmentation of skin lesions or nodules. However, this task is considerably challenging due to the elusive edges, irregular perimeters, and variations both within and across lesion classes. Despite numerous existing algorithms for segmenting skin lesions from dermoscopic images, they often fall short of industry benchmarks in terms of precision. To address this, our research introduces a novel U-Net++ architecture, implementing tailored adjustments to feature map dimensions. The aim is to significantly enhance automated segmentation precision for dermoscopic images. Our evaluation involved a comprehensive assessment of the model's performance, encompassing an exploration of various parameters such as epochs, batch size, and optimizer selections. Additionally, we conducted extensive testing using augmentation techniques to bolster the image volume within the HAM10000 dataset. A key innovation in our research is the integration of a hair removal process into the U-Net++ algorithm, significantly enhancing image quality and subsequently leading to improved segmentation accuracy. The results of our proposed method demonstrate substantial advancements, showcasing an impressive Mean Intersection over Union (IoU) of 84.1%, a Mean Dice Coefficient of 91.02%, and a Segmentation Test Accuracy of 95.10%. Our suggested U-Net++ algorithm does a better job of segmenting than U-Net, Modified U-Net, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). This shows that it could be used to improve dermoscopy image analysis. Our proposed algorithm shows remarkable improvement in both observational outcomes and classifier performance.","PeriodicalId":493889,"journal":{"name":"Review of computer engineering research","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139789793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cyber attacks on UAV networks: A comprehensive survey 对无人机网络的网络攻击：全面调查

Review of computer engineering research Pub Date : 2024-02-09 DOI: 10.18488/76.v11i1.3636

Ashish Mahalle, Sarika Khandelwal, Abhishek Dhore, Vishwajit Barbudhe, Vivek Waghmare

{"title":"Cyber attacks on UAV networks: A comprehensive survey","authors":"Ashish Mahalle, Sarika Khandelwal, Abhishek Dhore, Vishwajit Barbudhe, Vivek Waghmare","doi":"10.18488/76.v11i1.3636","DOIUrl":"https://doi.org/10.18488/76.v11i1.3636","url":null,"abstract":"New technologies are constantly emerging in the modern world and changing the way we live our everyday lives. Although technology has many useful applications, there are various ways it can potentially be abused. Unmanned aerial vehicles (UAVs) are one of the most rapidly developing technologies, with potentially far-reaching consequences. A new focus on UAV applications has fueled rising concerns with regards to security, specifically around networked UAVs. UAVs may be managed from a remote place with relative ease. Essential operations involving the use of military tactics and weapons involve employing them in a variety of situations, such as reconnaissance, surveillance, and offensive, defensive, and civilian capacities. Message insertion, message manipulation, jamming, and GPS spoofing are the most commonly used cyber-attacks against these systems. Ensuring the security of electronics and communications in systems that employ several UAVs is of utmost importance to guarantee their safety and dependability in military and civilian activities. Many technological methods have been developed over the past decade for securing UAVs from cyber-attacks. This paper attempts to summaries the problems that can arise with unmanned aerial vehicles (UAVs), cyber-attacks, and the countermeasures used to protect against them. This is the first paper that details all of the past cyber-attacks on unmanned aerial vehicles (UAVs).","PeriodicalId":493889,"journal":{"name":"Review of computer engineering research","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing skin lesion segmentation with U-Net++: Design, analysis, and performance evaluation 利用 U-Net++ 增强皮损分割：设计、分析和性能评估

Review of computer engineering research Pub Date : 2024-02-09 DOI: 10.18488/76.v11i1.3635

S. Patil, Hitendra D. Patil

{"title":"Enhancing skin lesion segmentation with U-Net++: Design, analysis, and performance evaluation","authors":"S. Patil, Hitendra D. Patil","doi":"10.18488/76.v11i1.3635","DOIUrl":"https://doi.org/10.18488/76.v11i1.3635","url":null,"abstract":"The present research examines the enhancement of skin lesion segmentation with U-Net++. Achieving accurate classification of dermoscopy images is heavily contingent on the precise segmentation of skin lesions or nodules. However, this task is considerably challenging due to the elusive edges, irregular perimeters, and variations both within and across lesion classes. Despite numerous existing algorithms for segmenting skin lesions from dermoscopic images, they often fall short of industry benchmarks in terms of precision. To address this, our research introduces a novel U-Net++ architecture, implementing tailored adjustments to feature map dimensions. The aim is to significantly enhance automated segmentation precision for dermoscopic images. Our evaluation involved a comprehensive assessment of the model's performance, encompassing an exploration of various parameters such as epochs, batch size, and optimizer selections. Additionally, we conducted extensive testing using augmentation techniques to bolster the image volume within the HAM10000 dataset. A key innovation in our research is the integration of a hair removal process into the U-Net++ algorithm, significantly enhancing image quality and subsequently leading to improved segmentation accuracy. The results of our proposed method demonstrate substantial advancements, showcasing an impressive Mean Intersection over Union (IoU) of 84.1%, a Mean Dice Coefficient of 91.02%, and a Segmentation Test Accuracy of 95.10%. Our suggested U-Net++ algorithm does a better job of segmenting than U-Net, Modified U-Net, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). This shows that it could be used to improve dermoscopy image analysis. Our proposed algorithm shows remarkable improvement in both observational outcomes and classifier performance.","PeriodicalId":493889,"journal":{"name":"Review of computer engineering research","volume":"167 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139849665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recurrent neural network implementation of digital integrated circuits to mitigate challenges in design verification 递归神经网络实现的数字集成电路，以减轻设计验证的挑战

Review of computer engineering research Pub Date : 2023-11-10 DOI: 10.18488/76.v10i3.3512

Ming Keat Yeong, Eric Tatt Wei Ho

{"title":"Recurrent neural network implementation of digital integrated circuits to mitigate challenges in design verification","authors":"Ming Keat Yeong, Eric Tatt Wei Ho","doi":"10.18488/76.v10i3.3512","DOIUrl":"https://doi.org/10.18488/76.v10i3.3512","url":null,"abstract":"Design verification is the dominant stage that consumes the most resources in the digital integrated circuit (IC) design process. Design verification is important because human designers imperfectly convert high-level specifications to low-level circuit implementations using standard cell logic, which is nonlinear and complex to predict and characterize. The widening process variations in shrinking process technologies while digital designs grow in scale and complexity to the extent of being impossible to fully or intuitively identify all temporal interactions of a specific design. Deep neural networks (DNN) are being progressively integrated into the sophisticated software tool chain and design process flow of digital IC as artificial intelligence can learn relationships and correlations in complex, high-dimensional, and multi-factorial problems. In this work, we propose to apply DNN to implement digital IC to minimize the complexity of digital design verification. We posit that DNN can learn to implement circuit functions directly from high-level specifications without requiring detailed specifications from the designer. Trained neural networks can be implemented on neuromorphic hardware to achieve greater power and compute efficiencies than the conventional standard cell implementation. We demonstrate that over 150 randomly generated finite state machines (FSM) can be learned effectively with Recurrent Neural Network (RNN) comprising Gated Recurrent Units (GRU) with different complexity as indicated by the number of states and inputs to the FSM. Our proposed methodology of learning RNN GRU implementations of FSM demonstrates a way forward to reduce the cost and effort of design verification, ultimately leading towards faster digital IC design cycles.","PeriodicalId":493889,"journal":{"name":"Review of computer engineering research","volume":" 45","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135187710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Creating a deep learning model using a Swin Transformer and tree growth optimisation to classify brain tumour 使用Swin Transformer和树生长优化创建深度学习模型，对脑肿瘤进行分类

Review of computer engineering research Pub Date : 2023-10-16 DOI: 10.18488/76.v10i3.3500

K Sankar, V Gokula Krishnan, S Sendil Kumar, P Pushpa, B Prathusha Laxmi

{"title":"Creating a deep learning model using a Swin Transformer and tree growth optimisation to classify brain tumour","authors":"K Sankar, V Gokula Krishnan, S Sendil Kumar, P Pushpa, B Prathusha Laxmi","doi":"10.18488/76.v10i3.3500","DOIUrl":"https://doi.org/10.18488/76.v10i3.3500","url":null,"abstract":"The brain, which has billions of cells, is the largest and most complex organ in the human body. A brain tumor is the primary malignant intracranial tumor of the central nervous system that develops most frequently. They are frequently found too late for effective therapy. The use of minimally invasive procedures is necessary to make a diagnosis and monitor a tumor of the central nervous system's response to therapy. There exist three distinct classifications of tumors, namely benign, premalignant, and malignant. This study concentrated on using deep learning to identify brain tumors (BT) using normal or abnormal brain pictures. Numerous methodologies have been employed to augment the quality of images, encompassing image smoothing and noise restoration procedures. The present study employs the proposed Adaptive Weighted Frost filter as it has been identified as the optimal approach for noise reduction in BT photographs. The Swin Transformer technology is employed for the purpose of classifying the BT. The efficiency of the Tree Growth Optimization (TGA) model for Swin transformer hyper parameter tweaking has been evaluated in this work. Before using our unique BT dataset for extensive experimental comparisons, medical specialists carefully examined it down to the pixel level. The predicted model achieved the greatest F1 score of 99.82% and the maximum accuracy, recall, and 100%, respectively.","PeriodicalId":493889,"journal":{"name":"Review of computer engineering research","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136182634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A secure and effective data aggregation in WSN for improved security and data privacy 一种安全有效的无线传感器网络数据聚合方法，提高了无线传感器网络的安全性和数据保密性

Review of computer engineering research Pub Date : 2023-10-06 DOI: 10.18488/76.v10i3.3494

S Suma Christal Mary, K Murugeswari, S Jyothi Shri, N Senthamilarasi

{"title":"A secure and effective data aggregation in WSN for improved security and data privacy","authors":"S Suma Christal Mary, K Murugeswari, S Jyothi Shri, N Senthamilarasi","doi":"10.18488/76.v10i3.3494","DOIUrl":"https://doi.org/10.18488/76.v10i3.3494","url":null,"abstract":"The increasing prevalence of internet usage and mobile devices has underscored the critical importance of safeguarding personal data. This is especially important in Wireless Sensor Networks (WSNs), where information typically requires in-network computing and collaborative processing. These computationally demanding approaches are not suitable for resource-constrained WSN nodes. Aggregating data effectively while protecting user data is a major challenge in wireless sensor networks. Many privacies of homomorphism encryption-based WSN data aggregation methods have been created and investigated recently. Since cluster leaders (aggregators) may rapidly combine cypher texts without decryption, communication overhead is reduced, making these data aggregation methods more secure than traditional ones. However, the base station only receives aggregated output, causing issues. Initial limits apply to aggregating functions. If the aggregated output is the sum of sensing data, the base station cannot acquire the maximum value. Second, attaching message digests or signatures to sensory samples does not allow the base station to validate data authenticity. In dangerous places, WSNs must be energy-efficient and private. In this research, we present a data aggregation method known as Energy-Efficient and Privacy-Preserving (E2P2). E2P2 data aggregation utilizes less energy and yields more accurate results. Private data aggregation with increased accuracy and hybrid encryption is presented in this research. The goal is to reduce data transmission and energy use collisions and offset collision-induced loss. Extensive simulations compare E2P2 to earlier approaches. Experimental results show that E2P2 outperforms other algorithms. Good exactness, complexity, and safety are demonstrated by theoretical and simulation results.","PeriodicalId":493889,"journal":{"name":"Review of computer engineering research","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134943644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image-based MRI detection of brain tumours using convolutional neural networks 基于图像的卷积神经网络脑肿瘤MRI检测

Review of computer engineering research Pub Date : 2023-10-06 DOI: 10.18488/76.v10i3.3495

G Preethi, Thangamma NG, S Perumal Sankar, Md Abul Ala Walid, D Suganthi, K Deepthika

{"title":"Image-based MRI detection of brain tumours using convolutional neural networks","authors":"G Preethi, Thangamma NG, S Perumal Sankar, Md Abul Ala Walid, D Suganthi, K Deepthika","doi":"10.18488/76.v10i3.3495","DOIUrl":"https://doi.org/10.18488/76.v10i3.3495","url":null,"abstract":"Rapid and uncontrolled cellular proliferation is what distinguishes a brain tumor. Unfortunately, brain tumors cannot be prevented other than via surgery. As predicted, deep learning may help diagnose and cure brain cancers. The segmentation approach has been widely studied for brain tumor removal. This uses the segmentation approach, one of the most advanced methods for object detection and categorization. To efficiently assess the tumor's size, an accurate and automated brain tumor segmentation approach is needed. We present a fully automated brain tumor separation method for imaging investigations. The approach has been developed with convolutional neural networks. The Multimodal Brain Tumor Image Segmentation (BRATS) datasets tested our strategy. This result suggests that DL should investigate heterogeneous MRI image segmentation to improve brain tumor segmentation accuracy and efficacy. This study may lead to more accurate medical diagnoses and treatments. Researchers in this study also found a way to automatically find cancerous tumours by using the Grey Level Co-Occurrence Matrix (GLCM) and discrete wavelet transform (DWT) to find features in MRI images. They then used a CNN to guess the final prognosis. The preceding section details this technique. When compared to the other algorithm, the CNN method uses computer resources better.","PeriodicalId":493889,"journal":{"name":"Review of computer engineering research","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134944330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variational model with image denoising fitting term for boundary extraction of breast ultrasound images 基于图像去噪拟合项的乳腺超声图像边界提取变分模型

Review of computer engineering research Pub Date : 2023-09-15 DOI: 10.18488/76.v10i2.3473

Nurdina Badrulhisam, Nurhuda Ismail, Abdul Kadir Jumaat, Mohd Azdi Maasar, Mohamed Faris Laham

{"title":"Variational model with image denoising fitting term for boundary extraction of breast ultrasound images","authors":"Nurdina Badrulhisam, Nurhuda Ismail, Abdul Kadir Jumaat, Mohd Azdi Maasar, Mohamed Faris Laham","doi":"10.18488/76.v10i2.3473","DOIUrl":"https://doi.org/10.18488/76.v10i2.3473","url":null,"abstract":"A variational model was used to extract or segment the breast ultrasound (BUS) image boundary in order to find a closed curve line of the abnormality region for further diagnosis. A recent selective variational model, termed the Convex Distance Selective Segmentation (CDSS) model, is effective at segmenting a specific image object. However, the CDSS model has difficulty segmenting noisy images. Unavoidable noise in BUS pictures leads to poor segmentation, as is widely recognized. The objective of this work is to propose a reformulation of the Convex Distance Selective Segmentation (CDSS) model for the purpose of segmenting BUS pictures. Consideration of four distinct image Denoising algorithms—Gaussian filter, Median filter, Wiener filter, and Rudin-Osher-Fatemi (ROF) algorithm—as the new fitting terms in the CDSS model leads to four variants of modified CDSS models called Modified CDSS based on Gaussian filter (MCDSSG), Modified CDSS based on Median filter (MCDSSM), Modified CDSS based on Wiener filter (MCDSSW) and Modified CDSS based on ROF (MCDSSROF). To solve the modified models, we first derived the associate Euler-Lagrange equation and solved it in Matrix Laboratory (MATLAB) software. Experiments demonstrated that the proposed MCDSSROF model based on the ROF denoising algorithm provided the highest average of Peak-Signal-To-Noise-Ratio (PSNR), Dice, and Jaccard Similarity Coefficients, indicating the highest denoising quality and segmentation accuracy in comparison to other models.","PeriodicalId":493889,"journal":{"name":"Review of computer engineering research","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135484594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bias field correction-based active contour model for region of interest extraction in digital image 基于偏置场校正的数字图像感兴趣区域提取主动轮廓模型

Review of computer engineering research Pub Date : 2023-09-15 DOI: 10.18488/76.v10i2.3471

Nurul Ain Suraya Rosli, Abdul Kadir Jumaat, Mohd Azdi Maasar, Mohamed Faris Laham, Normahirah Nek Abd Rahman

{"title":"Bias field correction-based active contour model for region of interest extraction in digital image","authors":"Nurul Ain Suraya Rosli, Abdul Kadir Jumaat, Mohd Azdi Maasar, Mohamed Faris Laham, Normahirah Nek Abd Rahman","doi":"10.18488/76.v10i2.3471","DOIUrl":"https://doi.org/10.18488/76.v10i2.3471","url":null,"abstract":"The region-based Active Contour Model (ACM) is a widely known variational segmentation model for extracting or segmenting a digital image into numerous sections for further analysis. Distinguishing between global and specific segmentation models within this paradigm is possible. The global segmentation model is incapable of selectively segmenting the region of interest (ROI) from the input image, which leads to an over-segmented problem. A variety of models have been devised to address the task of selective segmentation, which involves the extraction of the boundary of a particular region of interest (ROI) inside a digital image. The Primal Dual Selective Segmentation (PDSS) model has been recently introduced and exhibits significant potential in terms of accuracy. Nevertheless, the presence of intensity inhomogeneity in digital images disrupts the precision and localisation of target regions of segmentation. Therefore, it is important to take into account bias field adjustment, also known as correction for intensity inhomogeneity. So, this study came up with a new selective segmentation model called the Selective Segmentation with Bias Field Correction (SSBF) model by combining the idea of the existing PDSS model with the level set-based bias field correction technique. To solve the proposed SSBF model, we first derived the Euler-Lagrange (EL) equation and solved it in MATLAB software. The Intersection over Union (IOU) coefficient, also known as the Dice (DSC) and Jaccard (JSC) similarity metrics, evaluated the proposed model's accuracy. Experimental results demonstrate that the JSC and DSC values of the proposed model were 13.4% and 7.2% higher, respectively, than the competing model.","PeriodicalId":493889,"journal":{"name":"Review of computer engineering research","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135484590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0