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Fund","award":["313011AFG4"],"award-info":[{"award-number":["313011AFG4"]}]},{"name":"European Regional Development Fund","award":["TL02000313"],"award-info":[{"award-number":["TL02000313"]}]},{"name":"The Czech Science Foundation (TACR)","award":["CZ.02.1.01\/0.0\/0.0\/17 049\/0008441"],"award-info":[{"award-number":["CZ.02.1.01\/0.0\/0.0\/17 049\/0008441"]}]},{"name":"The Czech Science Foundation (TACR)","award":["SV4502261\/SP2022\/98"],"award-info":[{"award-number":["SV4502261\/SP2022\/98"]}]},{"name":"The Czech Science Foundation (TACR)","award":["CZ.01.1.02\/0.0\/0.0\/20_321\/0024858"],"award-info":[{"award-number":["CZ.01.1.02\/0.0\/0.0\/20_321\/0024858"]}]},{"name":"The Czech Science Foundation (TACR)","award":["313011AFG4"],"award-info":[{"award-number":["313011AFG4"]}]},{"name":"The Czech Science Foundation (TACR)","award":["TL02000313"],"award-info":[{"award-number":["TL02000313"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur\u2019s entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation\u2019s robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.<\/jats:p>","DOI":"10.3390\/s22176335","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T02:55:34Z","timestamp":1661309734000},"page":"6335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Performance and Robustness of Regional Image Segmentation Driven by Selected Evolutionary and Genetic Algorithms: Study on MR Articular Cartilage Images"],"prefix":"10.3390","volume":"22","author":[{"given":"Jan","family":"Kubicek","sequence":"first","affiliation":[{"name":"Department of Cybernetics and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17.listopadu 2172\/15, Poruba, 708 00 Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2788-7794","authenticated-orcid":false,"given":"Alice","family":"Varysova","sequence":"additional","affiliation":[{"name":"Department of Cybernetics and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17.listopadu 2172\/15, Poruba, 708 00 Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-2587","authenticated-orcid":false,"given":"Martin","family":"Cerny","sequence":"additional","affiliation":[{"name":"Department of Cybernetics and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17.listopadu 2172\/15, Poruba, 708 00 Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristyna","family":"Hancarova","sequence":"additional","affiliation":[{"name":"Department of Cybernetics and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17.listopadu 2172\/15, Poruba, 708 00 Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Oczka","sequence":"additional","affiliation":[{"name":"Department of Cybernetics and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17.listopadu 2172\/15, Poruba, 708 00 Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0165-7317","authenticated-orcid":false,"given":"Martin","family":"Augustynek","sequence":"additional","affiliation":[{"name":"Department of Cybernetics and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17.listopadu 2172\/15, Poruba, 708 00 Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9527-4642","authenticated-orcid":false,"given":"Marek","family":"Penhaker","sequence":"additional","affiliation":[{"name":"Department of Cybernetics and Biomedical Engineering, V\u0160B\u2014Technical University of Ostrava, 17.listopadu 2172\/15, Poruba, 708 00 Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ondrej","family":"Prokop","sequence":"additional","affiliation":[{"name":"MEDIN, a.s., Vlachovicka 619, 592 31 Nove Mesto na Morave, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2971-9313","authenticated-orcid":false,"given":"Radomir","family":"Scurek","sequence":"additional","affiliation":[{"name":"Department of Security Services, Faculty of Safety Engineering, V\u0160B\u2014Technical University of Ostrava, ul. Lumirova 3, 700 30 Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"ref_1","unstructured":"Deng, X., Zhang, H., and Yang, Y. (2022, January 25\u201326). Ultrasonic Image Segmentation Algorithm of Thyroid Nodules Based on DPCNN. Proceedings of the 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021), Birmingham, UK. Lecture Notes in Electrical Engineering."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"012001","DOI":"10.1088\/1742-6596\/2082\/1\/012001","article-title":"An effective approach for CT lung segmentation using region growing","volume":"2082","author":"Yang","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1007\/s10044-021-01021-8","article-title":"Superpixel\/voxel medical image segmentation algorithm based on the regional interlinked value","volume":"24","author":"Fang","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"ref_4","first-page":"1785","article-title":"Automated skull damage detection from assembled skull model using computer vision and machine learning","volume":"13","author":"Mangrulkar","year":"2021","journal-title":"Int. J. Inf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Le, N., Bui, T., Vo-Ho, V.-K., Yamazaki, K., and Luu, K. (2021). Narrow Band Active Contour Attention Model for Medical Segmentation. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081393"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102710","DOI":"10.1016\/j.bspc.2021.102710","article-title":"Graph weighting scheme for skin lesion segmentation in macroscopic images","volume":"68","author":"Filali","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107273","DOI":"10.1016\/j.asoc.2021.107273","article-title":"Level set approach based on Parzen Window and floor of log for edge computing object segmentation in digital images","volume":"105","author":"Marques","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1007\/s11548-021-02351-y","article-title":"Computer-aided diagnosis and regional segmentation of nasopharyngeal carcinoma based on multi-modality medical images","volume":"16","author":"Qi","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"034003","DOI":"10.1117\/1.JMI.8.3.034003","article-title":"Multi-class medical image segmentation using one-vs-rest graph cuts and majority voting","volume":"8","author":"Hu","year":"2021","journal-title":"J. Med. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Song, M., and Kim, Y. (2021, January 4\u20136). Manipulating Retinal OCT data for Image Segmentation based on Encoder-Decoder Network. Proceedings of the 2021 15th International Conference on Ubiquitous Information Management and Communication, IMCOM 2021, Seoul, Korea.","DOI":"10.1109\/IMCOM51814.2021.9377406"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Oda, M., and Mori, K. (2021, January 11\u201317). Graph Cuts Loss to Boost Model Accuracy and Generalizability for Medical Image Segmentation. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00369"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6088322","DOI":"10.1155\/2021\/6088322","article-title":"Application of MRI Image Based on Computer Semiautomatic Segmentation Algorithm in the Classification Prediction of Breast Cancer Histology","volume":"2021","author":"Sheng","year":"2021","journal-title":"J. Health Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cai, L.T., Baida, M., Wren-Jarvis, J., Bourla, I., and Mukherjee, P. (2021, January 1). Diffusion MRI Automated Region of Interest Analysis in Standard Atlas Space versus the Individual\u2019s Native Space. Proceedings of the Computational Diffusion MRI: 12th International Workshop, CDMRI 2021, Strasbourg, France.","DOI":"10.1007\/978-3-030-87615-9_10"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"129293","DOI":"10.1109\/ACCESS.2021.3113036","article-title":"Deep Regional Metastases Segmentation for Patient-Level Lymph Node Status Classification","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"116815","DOI":"10.1016\/j.eswa.2022.116815","article-title":"Adversarial attacks and defenses on AI in medical imaging informatics: A survey","volume":"198","author":"Kaviani","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Eseng\u00f6n\u00fcl, M., Marta, A., Beir\u00e3o, J., Pires, I.M., and Cunha, A. (2022). A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management. Medicina, 58.","DOI":"10.3390\/medicina58040504"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Loddo, A., and Putzu, L. (2022). On the Reliability of CNNs in Clinical Practice: A Computer-Aided Diagnosis System Case Study. Appl. Sci., 12.","DOI":"10.3390\/app12073269"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1053\/j.sult.2022.02.003","article-title":"Radiomics: A Primer on Processing Workflow and Analysis","volume":"43","author":"Avery","year":"2022","journal-title":"Semin. Ultrasound CT MRI"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104401","DOI":"10.1016\/j.imavis.2022.104401","article-title":"A review on 2D instance segmentation based on deep neural networks","volume":"120","author":"Gu","year":"2022","journal-title":"Image Vis. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5791","DOI":"10.1007\/s00521-022-06960-9","article-title":"Literature review: Efficient deep neural networks techniques for medical image analysis","volume":"34","author":"Abdou","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10278-021-00556-w","article-title":"Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation","volume":"35","author":"Jeong","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1007\/s10462-021-10082-4","article-title":"A comprehensive review of image analysis methods for microorganism counting: From classical image processing to deep learning approaches","volume":"55","author":"Zhang","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_23","first-page":"20552076221074122","article-title":"Magnetic resonance image-based brain tumour segmentation methods: A systematic review","volume":"8","author":"Bhalodiya","year":"2022","journal-title":"Digit. Health"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ahmed, S.M., and Mstafa, R.J. (2022). A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to Deep Learning. Diagnostics, 12.","DOI":"10.3390\/diagnostics12030611"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"200483","DOI":"10.1016\/j.fri.2021.200483","article-title":"Image segmentation of post-mortem computed tomography data in forensic imaging: Methods and applications","volume":"28","author":"Ebert","year":"2022","journal-title":"Forensic Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","article-title":"Domain Adaptation for Medical Image Analysis: A Survey","volume":"69","author":"Guan","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s12410-022-09563-z","article-title":"Radiomics in Cardiovascular Disease Imaging: From Pixels to the Heart of the Problem","volume":"15","author":"Spadarella","year":"2022","journal-title":"Curr. Cardiovasc. Imaging Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s12530-021-09371-8","article-title":"Notes on edge detection approaches","volume":"13","author":"Muntarina","year":"2022","journal-title":"Evol. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mishra, I., Aravinda, K., Kumar, J.A., Keerthi, C., Shree, R.D., and Srikumar, S. (2022, January 23\u201325). Medical Imaging using Signal Processing: A Comprehensive Review. Proceedings of the 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India.","DOI":"10.1109\/ICAIS53314.2022.9742778"},{"key":"ref_30","first-page":"522","article-title":"A Review: Recent Automatic Algorithms for the Segmentation of Brain Tumor MRI","volume":"Volume 105","author":"Boulouard","year":"2022","journal-title":"AI and IoT for Sustainable Development in Emerging Countries"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.neucom.2022.01.005","article-title":"Review the state-of-the-art technologies of semantic segmentation based on deep learning","volume":"493","author":"Mo","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"102013","DOI":"10.1016\/j.compmedimag.2021.102013","article-title":"A community-based approach to image analysis of cells, tissues and tumors","volume":"95","author":"Vizcarra","year":"2022","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1002\/uog.24804","article-title":"Evaluation of classic and novel ultrasound signs of placenta accreta spectrum","volume":"59","author":"Skupski","year":"2022","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_34","first-page":"105","article-title":"Medical Image Enhancement: A Review","volume":"288","author":"Radhika","year":"2022","journal-title":"Proc. Int. Conf. Data Sci. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"012040","DOI":"10.1088\/1742-6596\/2071\/1\/012040","article-title":"A Review on Edge Detection on Osteogenesis Imperfecta (OI) Image using Fuzzy Logic","volume":"2071","author":"Zaki","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1007\/s11831-021-09646-y","article-title":"State-of-the-Art Level Set Models and Their Performances in Image Segmentation: A Decade Review","volume":"29","author":"Biswas","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s10661-022-09945-2","article-title":"Application of \u201cOTSU\u201d\u2014An image segmentation method for differentiation of snow and ice regions of glaciers and assessment of mass budget in Chandra basin, Western Himalaya using Remote Sensing and GIS techniques","volume":"194","author":"Gaddam","year":"2022","journal-title":"Environ. Monit. Assess."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106968","DOI":"10.1016\/j.optlaseng.2022.106968","article-title":"Otsu-Kmeans gravity-based multi-spots center extraction method for microlens array imaging system","volume":"152","author":"Chen","year":"2022","journal-title":"Opt. Lasers Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Deng, Q., Shi, Z., and Ou, C. (2022). Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution. Entropy, 24.","DOI":"10.3390\/e24030319"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Uplaonkar, D.S., Virupakshappa, and Patil, N. (2022). Modified Otsu thresholding based level set and local directional ternary pattern technique for liver tumor segmentation. Int. J. Syst. Assur. Eng. Manag., 1\u201311.","DOI":"10.1007\/s13198-022-01637-x"},{"key":"ref_41","first-page":"5783","article-title":"Detection of Osteoarthritis Based on EHO Thresholding","volume":"71","author":"Kanthavel","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Mattheus, J., Grobler, H., and Abu-Mahfouz, A.M. (2020, January 25\u201327). A Review of Motion Segmentation: Approaches and Major Challenges. Proceedings of the 2020 2nd International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2020, Kimberley, South Africa.","DOI":"10.1109\/IMITEC50163.2020.9334076"},{"key":"ref_43","first-page":"15","article-title":"Border Detection in Skin Lesion Images Using an Improved Clustering Algorithm","volume":"16","author":"Jayalakshmi","year":"2020","journal-title":"Int. J. e-Collab."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1016\/j.bbe.2020.08.010","article-title":"A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia","volume":"40","author":"Anilkumar","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, Z., Guo, B., Lib, C., and Liu, H. (2020, January 27\u201329). Review on Superpixel Generation Algorithms Based on Clustering. Proceedings of the 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China.","DOI":"10.1109\/ICISCAE51034.2020.9236851"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hu, X., Chen, Q., Ye, X., Zhang, D., Tang, Y., and Ye, J. (2021). Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features. Symmetry, 13.","DOI":"10.3390\/sym13122325"},{"key":"ref_47","first-page":"1048","article-title":"Brain stroke computed tomography images analysis using image processing: A Review","volume":"10","author":"Ali","year":"2021","journal-title":"IAES Int. J. Artif. Intell. (IJAI)"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kordt, J., Brachmann, P., Limberger, D., and Lippert, C. (2021, January 8\u201312). Interactive Volumetric Region Growing for Brain Tumor Segmentation on MRI using WebGL. Proceedings of the Web3D 2021: 26th ACM International Conference on 3D Web Technology, Pisa, Italy.","DOI":"10.1145\/3485444.3487640"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1007\/s12652-021-03544-8","article-title":"Hybrid algorithms for brain tumor segmentation, classification and feature extraction","volume":"13","author":"Habib","year":"2022","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/978-3-030-91738-8_3","article-title":"Hybrid Mammogram Segmentation Using Watershed and Region Growing","volume":"Volume 357","author":"Maleh","year":"2022","journal-title":"Advances in Information, Communication and Cybersecurity"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/978-981-16-3945-6_23","article-title":"Wavelets and Convolutional Neural Networks-Based Automatic Segmentation and Prediction of MRI Brain Images","volume":"251","author":"Krishnammal","year":"2022","journal-title":"IOT Smart Syst."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Su, M., Shi, W., Zhao, D., Cheng, D., and Zhang, J. (2022). A High-Precision Method for Segmentation and Recognition of Shopping Mall Plans. Sensors, 22.","DOI":"10.3390\/s22072510"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Luisi, J.D., Lin, J.L., Ameredes, B.T., and Motamedi, M. (2022). Spatial-Temporal Speckle Variance in the En-Face View as a Contrast for Optical Coherence Tomography Angiography (OCTA). Sensors, 22.","DOI":"10.3390\/s22072447"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Khan, A.-M., Haque, F., Hasan, K.R., Alajmani, S.H., Baz, M., Masud, M., and Nahid, A.-A. (2022). LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning. Sensors, 22.","DOI":"10.3390\/s22155595"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bai, K., Wang, J., and Wang, H. (2021). A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information. Sensors, 21.","DOI":"10.3390\/s21124209"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Shia, W.-C., Hsu, F.-R., Dai, S.-T., Guo, S.-L., and Chen, D.-R. (2022). Semantic Segmentation of the Malignant Breast Imaging Reporting and Data System Lexicon on Breast Ultrasound Images by Using DeepLab v3+. Sensors, 22.","DOI":"10.3390\/s22145352"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Giang, T.T.H., Khai, T.Q., Im, D.-Y., and Ryoo, Y.-J. (2022). Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images. Sensors, 22.","DOI":"10.3390\/s22145140"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ciecholewski, M., and Kassja\u0144ski, M. (2021). Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review. Sensors, 21.","DOI":"10.3390\/s21062027"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhang, F., Li, L., Lin, Y., Zhang, Z., Shi, L., Tao, H., and Qin, T. (2021). Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion. Sensors, 21.","DOI":"10.3390\/s21237945"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhu, M., Wang, J., Guo, X., Yang, Y., and Wang, J. (2022). Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation. Sensors, 22.","DOI":"10.3390\/s22114222"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kweon, J., Yoo, J., Kim, S., Won, J., and Kwon, S. (2022). A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation. Sensors, 22.","DOI":"10.3390\/s22103960"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yamanakkanavar, N., Choi, J.Y., and Lee, B. (2022). Multiscale and Hierarchical Feature-Aggregation Network for Segmenting Medical Images. Sensors, 22.","DOI":"10.3390\/s22093440"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ali, R., Hardie, R.C., Narayanan, B.N., and Kebede, T.M. (2022). IMNets: Deep Learning Using an Incremental Modular Network Synthesis Approach for Medical Imaging Applications. Appl. Sci., 12.","DOI":"10.3390\/app12115500"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Jimenez-Casta\u00f1o, C.A., \u00c1lvarez-Meza, A.M., Aguirre-Ospina, O.D., C\u00e1rdenas-Pe\u00f1a, D.A., and Orozco-Guti\u00e9rrez, A. (2021). Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation. Sensors, 21.","DOI":"10.3390\/s21227741"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ali, R., Hardie, R.C., and Ragb, H.K. (2020, January 13\u201315). Ensemble Lung Segmentation System Using Deep Neural Networks. Proceedings of the 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA.","DOI":"10.1109\/AIPR50011.2020.9425311"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_67","first-page":"397","article-title":"Extraction of Blood Vessels Using Multilevel Thresholding with Color Coding","volume":"Volume 362","author":"Sulaiman","year":"2015","journal-title":"Advanced Computer and Communication Engineering Technology"},{"key":"ref_68","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"Volume 5.1","author":"MacQueen","year":"1967","journal-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0734-189X(85)90125-2","article-title":"A new method for gray-level picture thresholding using the entropy of the histogram","volume":"29","author":"Kapur","year":"1985","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Lang, C., and Jia, H. (2019). Kapur\u2019s Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm. Entropy, 21.","DOI":"10.3390\/e21030318"},{"key":"ref_71","unstructured":"(2022, May 15). NIMH Data Archive\u2014OAI (The Osteoarthritis Initiative). National Institutes of Health. U.S. Department of Health and Human Services, Available online: https:\/\/nda.nih.gov\/oai\/."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"7653","DOI":"10.1007\/s00330-021-07853-6","article-title":"Automated cartilage segmentation and quantification using 3D ultrashort echo time (UTE) cones MR imaging with deep convolutional neural networks","volume":"31","author":"Xue","year":"2021","journal-title":"Eur. Radiol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6335\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:14:07Z","timestamp":1760141647000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,23]]},"references-count":72,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176335"],"URL":"https:\/\/doi.org\/10.3390\/s22176335","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,23]]}}}