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A comprehensive survey on segmentation techniques for retinal vessel segmentation

NEUROCOMPUTING 2023年第1期556卷

作者： Cervantes, Jair Cervantes, Jared Garcia-Lamont, Farid Yee-Rendon, Arturo Cabrera, Josue Espejel Jalili, Laura Dominguez UAEMEX Autonomous Univ Mexico State Texcoco 56259 Mexico Univ Autonoma Sinaloa Fac Informat Culiacan Culiacan 80013 Sinaloa Mexico

In recent years, enormous research has been carried out on the segmentation of blood vessels. segmentation of blood vessels in retinal images is crucial for diagnosing, treating, evaluating clinical results, and early detection of eye disorders. A successful segmentation accurately reflects the blood vessels' structure and helps obtain patterns that can be used to identify retinal disorders and diseases. Most recent research on vessel segmentation employs multiple processes to determine the appropriate segmentation. Finding the best techniques for segmentation is a complex process. In certain circumstances, it requires a thorough understanding of every step that can only be acquired through years of training. Comprehension and expertise in segmentation procedures are essential for accurate segmentation. This paper briefly introduces the segmentation of blood vessels in retinal images, describes many preprocessing and segmentation techniques, and summarizes challenges and trends. Furthermore, the limitations of the current systems will be identified.

关键词： Vessel segmentation Preprocessing techniques segmentation techniques

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A Survey: Image segmentation techniques 4th

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Phonsa, Gurbakash Manu, K. Mewar Univ Chittaurgarh 312901 Rajasthan India Lovely Profess Univ Jalandhar 144403 Punjab India

ISBN: (纸本)9789811307614;9789811307607

Image segmentation is a core step of image processing. Image segmentation is an image processing technique distinct as in which we divide the image into multiple regions in forms of pixel or break down the image into different objects and regions. The main aim of image segmentation is to represent an image on noise-free form. The main use of image segmentation is to detect the objects, relevant data in digital image and their boundaries. These techniques split the image into small regions in order to analyse them and it is also helpful to distinguish the different types of object in single image. Till date, various image segmentation approaches are suggested by researchers with specific aspects or little diversity and commitment for betterment. Researchers work continuously to optimize techniques and enhancing them in order to make image more recognizable, i.e. more smother and noise free. In this paper, as per our study, different researcher works on image processing approaches like thresholding, neural network, edge base segmentation, region base segmentation, etc. are presented.

关键词： segmentation techniques Edge detection Boundary-based segmentation Region-based segmentation

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Classification of Melanoma Using Different segmentation techniques 9th

Classification of Melanoma Using Different Segmentation Tech...

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9th International Conference on Innovations in Bio- Inspired Computing and Applications (IBICA) / 7th World Congress on Information and Communication Technologies (WICT)

作者： Gulati, Savy Bhogal, Rosepreet Kaur Lovely Profess Univ Phagwara India

ISBN: (纸本)9783030166816;9783030166809

Malignant melanoma is the most dangerous type of skin cancer which has capability to spread to other parts of body and proves deadly for a person. Mortality rates of melanoma are quite high. Also, number of melanoma patients is inclining at a faster pace. In such scenarios, Computer aided systems can act as savior because early detection of melanoma increases chances of survival. These CAD systems are based on image processing techniques. Among these techniques, segmentation is the most challenging task because melanoma moles appear in random patterns having ragged and irregular shapes. In this work, classification of melanoma and non-melanoma is performed using four different segmentation techniques namely otsu, k-means clustering, maximum entropy and active contour individually. Comparison of results obtained using all these techniques has been carried out and it is seen that among these techniques active contour-based method proves eminent in efficient classification of melanoma and non-melanoma by showing accuracy of 94.5%.

关键词： Melanoma Non-melanoma segmentation techniques Classification

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A comparative study of segmentation techniques for the quantification of brain subcortical volume

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BRAIN IMAGING AND BEHAVIOR 2018年第6期12卷 1678-1695页

作者： Akudjedu, Theophilus N. Nabulsi, Leila Makelyte, Migle Scanlon, Cathy Hehir, Sarah Casey, Helen Ambati, Srinath Kenney, Joanne O'Donoghue, Stefani McDermott, Emma Kilmartin, Liam Dockery, Peter McDonald, Colm Hallahan, Brian Cannon, Dara M. Natl Univ Ireland Galway Coll Med Nursing & Hlth SciPsychiat & Anat Ctr Neuroimaging & Cognit Genom NICOGSch Med Clin Neuroimaging LabNCBES Galway Neurosci Ctr Galway H91 TK33 Ireland Natl Univ Ireland Galway Coll Engn & Informat Galway H91 TK33 Ireland

Manual tracing of magnetic resonance imaging (MRI) represents the gold standard for segmentation in clinical neuropsychiatric research studies, however automated approaches are increasingly used due to its time limitations. The accuracy of segmentation techniques for subcortical structures has not been systematically investigated in large samples. We compared the accuracy of fully automated [(i) model-based: FSL-FIRST;(ii) patch-based: volBrain], semi-automated (FreeSurfer) and stereological (Measure (R)) segmentation techniques with manual tracing (ITK-SNAP) for delineating volumes of the caudate (easy-to-segment) and the hippocampus (difficult-to-segment). High resolution 1.5T T1-weighted MR images were obtained from 177 patients with major psychiatric disorders and 104 healthy participants. The relative consistency (partial correlation), absolute agreement (intraclass correlation coefficient, ICC) and potential technique bias (Bland-Altman plots) of each technique was compared with manual segmentation. Each technique yielded high correlations (0.77-0.87, p<0.0001) and moderate ICC's (0.28-0.49) relative to manual segmentation for the caudate. For the hippocampus, stereology yielded good consistency (0.52-0.55, p<0.0001) and ICC (0.47-0.49), whereas automated and semi-automated techniques yielded poor ICC (0.07-0.10) and moderate consistency (0.35-0.62, p<0.0001). Bias was least using stereology for segmentation of the hippocampus and using FreeSurfer for segmentation of the caudate. In a typical neuropsychiatric MRI dataset, automated segmentation techniques provide good accuracy for an easy-to-segment structure such as the caudate, whereas for the hippocampus, a reasonable correlation with volume but poor absolute agreement was demonstrated. This indicates manual or stereological volume estimation should be considered for studies that require high levels of precision such as those with small sample size.

关键词： segmentation techniques Stereology Subcortical structures FreeSurfer FSL-FIRST VolBrain

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Application of segmentation techniques in Styling of Leather Garments

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China Textile 2016年第2期 25-27页

作者： Zhengzheng Xu

The Fifth Plenary Session of the 18 th Communist Party of China Central Committee adopted proposals for economic and social development in the 13 th Five-Year Program(2016-2020)in October,2015,putting forward that China should highlight "innovation,coordination,the environment,opening up and sharing" to fulfill its economic *** five concepts are undoubtedly the main theme of China’s economic and social development in the next five years or even for a longer *** the textile industry,how to have a better understanding of these five concepts? And how can these five concepts be infiltrated into the development process of the textile industry in the next five years?

关键词： segmentation techniques Fashion Styling Leather garments Kinds The structural design

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Survey on Computational techniques for Pigmented Skin Lesion segmentation

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OPTICAL MEMORY AND NEURAL NETWORKS 2022年第4期31卷 333-366页

作者： Khanra, Shubhasis Kuila, Maitreyee Patra, Shibaprasad Saha, Ramesh Dhal, Krishna Gopal Midnapore Coll Autonomous Dept Comp Sci & Applicat Paschim Medinipur 721102 West Bengal India VIT Bhopal Univ Dept Comp Sci & Engn Bhopal 466114 Madhya Pradesh India

Skin lesion segmentation is the first step in skin lesion assessment, and it can help with the following classification task. It is a complex job because the borders of pigment regions may be hazy and the whole lesion can sometimes be the same color. Therefore, this paper provides a comprehensive review of the methodologies proposed in research papers published since 2017. Concerning the image segmentation task, the techniques were classified based on their principle of operation. The works are looked at with the help of certain datasets, year-by-year publications, performance evaluation measures, and other tools. The survey concluded that many researchers have successfully highlighted image segmentation of skin lesions;moreover, improved methods are needed to enhance the performance.

关键词： skin lesion melanoma image segmentation segmentation techniques machine learning

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Terrain detection and segmentation for autonomous vehicle navigation: A state-of-the-art systematic review

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INFORMATION FUSION 2025年 113卷

作者： Kabir, Md Mohsin Jim, Jamin Rahman Istenes, Zoltan Eotvos Lorand Univ Fac Informat H-1117 Budapest Hungary Univ Girona Super Polytech Sch Girona 17001 Spain Adv Machine Intelligence Res Lab Dhaka 1216 Bangladesh

This review comprehensively investigates the current state and emerging trends of autonomous vehicle terrain detection and segmentation. By systematically reviewing literature from various databases, this study outlines the evolution of detection and segmentation techniques from traditional computer vision methods to advanced machine learning and deep learning approaches. It identifies critical technological advancements, evaluates their performance, and discusses the challenges faced under various environmental conditions, data acquisition, and integration with vehicle systems. This study also highlights the need for standardized benchmarks and datasets to facilitate the development and testing of robust terrain detection systems. This review encompasses terrain detection and segmentation in structured environments, such as urban roads and highways, and unstructured environments, including rural paths and off-road terrains, to comprehensively analyze autonomous vehicle navigation challenges. By analyzing recent research findings, this review provides insights into future directions for overcoming these limitations and fostering innovation in the autonomous driving domain.

关键词： Autonomous vehicles Terrain detection segmentation techniques Machine learning Deep learning Computer vision

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Advanced segmentation method for integrating multi-omics data for early cancer detection

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EGYPTIAN INFORMATICS JOURNAL 2025年 29卷

作者： Sangeetha, S. K. B. Mathivanan, Sandeep Kumar Azath, M. Beniwal, Ravinder Ahmad, Naim Ghribi, Wade Mallik, Saurav SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai India Galgotias Univ Sch Comp Sci & Engn Greater Noida 203201 India KR Mangalam Univ Sch Engn & Technol Sohna Rd Gurugram India King Khalid Univ Coll Comp Sci Abha Saudi Arabia Harvard TH Chan Sch Publ Hlth Dept Environm Hlth Boston MA 02115 USA Univ Arizona Dept Pharmacol & Toxicol Tucson AZ 85721 USA

The global burden of cancer underscores the critical need for early diagnosis. Traditional diagnostic methods, relying on single biomarkers or imaging, often lack comprehensive predictive accuracy. Existing systems often focus on one or two types of omics data, such as genome or transcriptome, but do not comprehensively integrate multiple omics layers (genomic, transcriptomic, proteomic, and epigenomic). This limitation restricts the ability to capture the full biological complexity and heterogeneity of cancer, which can be critical for accurate prediction and understanding of disease mechanisms. We propose an advanced cancer prediction method called Integrated Multi-Omics segmentation (IMOS), which enhances the processing of multi-omics data by integrating genomic, transcriptomic, proteomic, and epigenomic information. IMOS segments data into biologically meaningful regions, facilitating more precise analysis. IMOS achieves outstanding performance with an average precision of 92 %, sensitivity of 88 %, and specificity of 94 %, outperforming traditional methods by 15 % in precision, 10 % in sensitivity, and 8 % in specificity. Validation using the Genomic Data Commons (GDC) dataset, encompassing diverse cancer types, demonstrated IMOS's robustness with accuracy of 91 %, sensitivity of 87 %, and specificity of 93 %. The system also excels in clustering evaluation, with a silhouette score ranging from 0.55 to 0.62 and the lowest Davies-Bouldin index achieved with three clusters.

关键词： Cancer Multi-omics segmentation techniques Precision oncology Early detection

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Evaluating the Performance of Various segmentation techniques in Industrial Radiographs

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CYBERNETICS AND INFORMATION TECHNOLOGIES 2014年第1期14卷 161-171页

作者： Thirugnanam, Mythili Anouncia, S. Margret VIT Univ SCSE Vellore 632014 Tamil Nadu India

At present, image processing concepts are widely used in different fields, such as remote sensing, communication, medical imaging, forensics and industrial inspection. Image segmentation is one of the key processes in image processing key stages. segmentation is a process of extracting various features of the image which can be merged or split to build the object of interest, on which image analysis and interpretation can be performed. Many researchers have proposed various segmentation algorithms to extract the region of interest from an image in various domains. Each segmentation algorithm has its own pros and cons based on the nature of the image and its quality. Especially, extracting a region of interest from a gray scale image is incredibly complex compared to colour images. This paper attempts to perform a study of various widely used segmentation techniques in gray scale images, mostly in industrial radiographic images that would help the process of defects detection in non-destructive testing.

关键词： segmentation techniques weld radio graphic images image analysis

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Virtual registration of comminuted bone fracture and preoperative assessment of reconstructed bone model using the Procrustes algorithm based on CT dataset

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE 2024年第2期238卷 219-236页

作者： Arumugam, Senthilmurugan Ranganathan, Rajesh Narayanasamy, Venkatesh Kumar Coimbatore Inst Technol Dept Mech Engn Coimbatore Tamil Nadu India PSG Inst Med Sci & Res Dept Orthopaed & Trauma Coimbatore Tamil Nadu India Coimbatore Inst Technol Dept Mech Engn Avinashi Rd Coimbatore 641014 Tamil Nadu India

A research work was undergone in a virtual bone reduction process for reconstruction of the comminuted pelvic bone fracture using a CT scan dataset of patients. This includes segmentation, 3D model optimization and bone registration technique. The accuracy of the reconstructed bone model was validated using Finite Element Method. Analysed and applied various segmentation techniques to segregate the injured bone structure. The ICP (Iterative Closest Point), Procrustes algorithm and Canny edge detection algorithm were applied to understand the bone registration process for surgery in detail. The average RMS error, mean absolute distance, mean absolute deviation, and mean signed distance of the reconstructed bone model using proposed algorithms involving 10 patient datasets in a group were found to be 1.77, 1.48, 1.51 and -0.31 mm respectively. The calculated RMS error value proved minimal error in semi-automatic registration than other existing automatic registration techniques. Therefore, the proposed approach is suitable for virtual bone reduction for comminuted pelvic bone fracture. This method could also be implemented for various other bone fracture reconstruction requirements.

关键词： Bone reconstruction virtual bone reduction process comminuted bone fracture segmentation techniques iterative closest point Procrustes algorithm

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