{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T03:56:56Z","timestamp":1769918216475,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T00:00:00Z","timestamp":1661558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871380"],"award-info":[{"award-number":["61871380"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["4172034"],"award-info":[{"award-number":["4172034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2020MF019"],"award-info":[{"award-number":["ZR2020MF019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["61871380"],"award-info":[{"award-number":["61871380"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4172034"],"award-info":[{"award-number":["4172034"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2020MF019"],"award-info":[{"award-number":["ZR2020MF019"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["61871380"],"award-info":[{"award-number":["61871380"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["4172034"],"award-info":[{"award-number":["4172034"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2020MF019"],"award-info":[{"award-number":["ZR2020MF019"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Automated segmentation of brain tumors is a difficult procedure due to the variability and blurred boundary of the lesions. In this study, we propose an automated model based on Bendlet transform and improved Chan-Vese (CV) model for brain tumor segmentation. Since the Bendlet system is based on the principle of sparse approximation, Bendlet transform is applied to describe the images and map images to the feature space and, thereby, first obtain the feature set. This can help in effectively exploring the mapping relationship between brain lesions and normal tissues, and achieving multi-scale and multi-directional registration. Secondly, the SSIM region detection method is proposed to preliminarily locate the tumor region from three aspects of brightness, structure, and contrast. Finally, the CV model is solved by the Hermite-Shannon-Cosine wavelet homotopy method, and the boundary of the tumor region is more accurately delineated by the wavelet transform coefficient. We randomly selected some cross-sectional images to verify the effectiveness of the proposed algorithm and compared with CV, Ostu, K-FCM, and region growing segmentation methods. The experimental results showed that the proposed algorithm had higher segmentation accuracy and better stability.<\/jats:p>","DOI":"10.3390\/e24091199","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T01:39:19Z","timestamp":1661737159000},"page":"1199","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model"],"prefix":"10.3390","volume":"24","author":[{"given":"Kexin","family":"Meng","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piercarlo","family":"Cattani","sequence":"additional","affiliation":[{"name":"Department of Computer, Control and Management Engineering, University of Rome \u201cLa Sapienza\u201d, Via Ariosto 25, 00185 Roma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6545-4589","authenticated-orcid":false,"given":"Francesco","family":"Villecco","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.3934\/mbe.2022119","article-title":"Mathematical modeling of therapeutic neural stem cell migration in mouse brain with and without brain tumors","volume":"19","author":"Gomez","year":"2022","journal-title":"Math. Biosci. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.media.2018.09.001","article-title":"Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture","volume":"50","author":"Xu","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.media.2017.10.002","article-title":"A deep learning model integrating FCNNs and CRFs for brain tumor segmentation","volume":"43","author":"Zhao","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102180","DOI":"10.1016\/j.artmed.2021.102180","article-title":"A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images","volume":"121","author":"Jiang","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zheng, R., Wang, Q., Lv, S., Li, C., Wang, C., Chen, W., and Wang, H. (2022). Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM. IEEE Trans. Med. Imaging.","DOI":"10.1109\/TMI.2022.3175461"},{"key":"ref_6","unstructured":"Jiang, H., Diao, Z., and Yao, Y.D. (2022). Deep Learning Techniques for Tumor Segmentation: A Review, Springer US."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Deantonio, L., Vigna, L., Paolini, M., Matheoud, R., Sacchetti, G.M., Masini, L., Loi, G., Brambilla, M., and Krengli, M. (2022). Application of a smart [18F] FDG-PET adaptive threshold segmentation algorithm for the biological target volume delineation in head and neck cancer. Q. J. Nucl. Med. Mol. Imaging.","DOI":"10.23736\/S1824-4785.22.03405-7"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"055026","DOI":"10.1088\/2057-1976\/ac13ba","article-title":"X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing","volume":"7","author":"Rodrigues","year":"2021","journal-title":"Biomed. Phys. Eng. Express"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.4018\/IJEHMC.20210501.oa4","article-title":"Towards better segmentation of abnormal part in multimodal images using kernel possibilistic C means particle swarm optimization with morphological reconstruction filters: Combination of KFCM and PSO with morphological filters","volume":"12","author":"Sumathi","year":"2021","journal-title":"Int. J. E-Health Med. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"347","DOI":"10.5573\/IEIESPC.2019.8.5.347","article-title":"An Error Resilience and Concealment Method for Line-based Wavelet Image Compressions","volume":"8","author":"Kim","year":"2019","journal-title":"IEIE Trans. Smart Process Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e25896","DOI":"10.1002\/qua.25896","article-title":"NMR relaxation and relaxivity parameters of MRI probes revealed by optimal wavelet signal compression of molecular dynamics simulations","volume":"119","author":"Santos","year":"2019","journal-title":"Int. J. Quantum Chem."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TMI.2018.2853808","article-title":"Efficient Enhancement of Stereo Endoscopic Images Based on Joint Wavelet Decomposition and Binocular Combination","volume":"38","author":"Sdiri","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TCSVT.2020.3010627","article-title":"Wavelet-based deep auto encoder-decoder (wdaed)-based image compression","volume":"31","author":"Mishra","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1109\/TMI.2014.2374354","article-title":"Three dimensional data-driven multi scale atomic representation of optical coherence tomography","volume":"34","author":"Kafieh","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11891","DOI":"10.1109\/TPEL.2020.2989401","article-title":"Two-Step HNN-Based Pattern Recognition Combining DWT-Based Multi-Resolution Analysis for Rechargeable Cells Distinction","volume":"35","author":"Lee","year":"2020","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_16","first-page":"694","article-title":"Multimodal Image Fusion Using Curvelet and Genetic Algorithm","volume":"76","author":"Gattim","year":"2017","journal-title":"J. Sci. Ind. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11554-014-0400-7","article-title":"CT and MRI image compression using wavelet-based contourlet transform and binary array technique","volume":"13","author":"Nadarajan","year":"2017","journal-title":"J. Real-Time Image Process"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1109\/TCSVT.2018.2817642","article-title":"Multi-Script-Oriented Text Detection and Recognition in Video\/Scene\/Born Digital Images","volume":"29","author":"Raghunandan","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/TSUSC.2018.2883822","article-title":"Automated Diagnosis of Pathological Brain Using Fast Curvelet Entropy Features","volume":"5","author":"Nayak","year":"2020","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"ref_20","unstructured":"Raikar, V.P., and Kwartowitz, D.M. (2016, January 1). Towards predictive diagnosis and management of rotator cuff disease: Using curvelet transform for edge detection and segmentation of tissue. Proceedings of the Medical Imaging 2016: Ultrasonic Imaging and Tomography, San Diego, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.acha.2005.07.002","article-title":"Wavelets with composite dilations and their MRA properties","volume":"20","author":"Guo","year":"2006","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/TIM.2019.2902808","article-title":"Multimodal Medical Image Sensor Fusion Model Using Sparse K-SVD Dictionary Learning in Nonsubsampled Shearlet Domain","volume":"69","author":"Singh","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3367","DOI":"10.1109\/TIM.2018.2877285","article-title":"Image Fusion Using Adjustable Non-subsampled Shearlet Transform","volume":"68","author":"Vishwakarma","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.acha.2017.06.002","article-title":"Bendlets: A second-order shearlet transform with bent elements","volume":"46","author":"Lessig","year":"2019","journal-title":"Appl. Comput. Harmon Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s12190-017-1120-5","article-title":"Study of shearlet transform using block matrix dilation","volume":"56","author":"Amiri","year":"2018","journal-title":"J. Appl. Math. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Casillo, M., Gupta, B.B., Lombardi, M., Lorusso, A., Santaniello, D., and Valentino, C. (2022). Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias. Electronics, 11.","DOI":"10.3390\/electronics11071003"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mei, S., Liu, M., Kudreyko, A., Cattani, P., Baikov, D., and Villecco, F. (2022). Bendlet Transform Based Adaptive Denoising Method for Microsection Images. Entropy, 24.","DOI":"10.3390\/e24070869"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ooi, A.Z.H., Embong, Z., Hamid, A.I.A., Zainon, R., Wang, S.L., Ng, T.F., Hamzah, R.A., Teoh, S.S., and Ibrahim, H. (2021). Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector. Sensors, 21.","DOI":"10.3390\/s21196380"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S0219691318500212","article-title":"Shannon-Cosine wavelet spectral method for solving fractional Fokker-Planck equations","volume":"16","author":"Mei","year":"2018","journal-title":"Int. J. Wavelets Multiresolution Inf. Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105254","DOI":"10.1016\/j.compag.2020.105254","article-title":"Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion","volume":"170","author":"Mao","year":"2020","journal-title":"Comput. Electron. Agric."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/9\/1199\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:16:24Z","timestamp":1760141784000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/9\/1199"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,27]]},"references-count":30,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["e24091199"],"URL":"https:\/\/doi.org\/10.3390\/e24091199","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,27]]}}}