{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T14:38:26Z","timestamp":1773499106617,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>A brain tumor is one of the foremost reasons for the rise in mortality among children and adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces that regulate growth inside the brain. A brain tumor appears when one type of cell changes from its normal characteristics and grows and multiplies abnormally. The unusual growth of cells within the brain or inside the skull, which can be cancerous or non-cancerous has been the reason for the death of adults in developed countries and children in under developing countries like Ethiopia. The studies have shown that the region growing algorithm initializes the seed point either manually or semi-manually which as a result affects the segmentation result. However, in this paper, we proposed an enhanced region-growing algorithm for the automatic seed point initialization. The proposed approach\u2019s performance was compared with the state-of-the-art deep learning algorithms using the common dataset, BRATS2015. In the proposed approach, we applied a thresholding technique to strip the skull from each input brain image. After the skull is stripped the brain image is divided into 8 blocks. Then, for each block, we computed the mean intensities and from which the five blocks with maximum mean intensities were selected out of the eight blocks. Next, the five maximum mean intensities were used as a seed point for the region growing algorithm separately and obtained five different regions of interest (ROIs) for each skull stripped input brain image. The five ROIs generated using the proposed approach were evaluated using dice similarity score (DSS), intersection over union (IoU), and accuracy (Acc) against the ground truth (GT), and the best region of interest is selected as a final ROI. Finally, the final ROI was compared with different state-of-the-art deep learning algorithms and region-based segmentation algorithms in terms of DSS. Our proposed approach was validated in three different experimental setups. In the first experimental setup where 15 randomly selected brain images were used for testing and achieved a DSS value of 0.89. In the second and third experimental setups, the proposed approach scored a DSS value of 0.90 and 0.80 for 12 randomly selected and 800 brain images respectively. The average DSS value for the three experimental setups was 0.86.<\/jats:p>","DOI":"10.3390\/jimaging7020022","type":"journal-article","created":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T11:40:48Z","timestamp":1612179648000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["Enhanced Region Growing for Brain Tumor MR Image Segmentation"],"prefix":"10.3390","volume":"7","author":[{"given":"Erena Siyoum","family":"Biratu","sequence":"first","affiliation":[{"name":"College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5118-0812","authenticated-orcid":false,"given":"Friedhelm","family":"Schwenker","sequence":"additional","affiliation":[{"name":"Institute of Neural Information Processing, Ulm University, 89081 Ulm, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0876-2021","authenticated-orcid":false,"given":"Taye Girma","family":"Debelee","sequence":"additional","affiliation":[{"name":"College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia"},{"name":"Artificial Intelligence Center, Addis Ababa 40782, Ethiopia"}]},{"given":"Samuel Rahimeto","family":"Kebede","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, Addis Ababa 40782, Ethiopia"},{"name":"Department of Electrical and Computer Engineering, Debreberhan University, Debre Berhan 445, Ethiopia"}]},{"given":"Worku Gachena","family":"Negera","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, Addis Ababa 40782, Ethiopia"}]},{"given":"Hasset Tamirat","family":"Molla","sequence":"additional","affiliation":[{"name":"College of Natural and Computational Science, Addis Ababa University, Addis Ababa 1176, Ethiopia"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sonar, P., Bhosle, U., and Choudhury, C. (2017, January 28\u201329). Mammography classification using modified hybrid SVM-KNN. Proceedings of the 2017 International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India.","DOI":"10.1109\/CSPC.2017.8305858"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"020022","DOI":"10.1063\/1.4954535","article-title":"Haralick texture and invariant moments features for breast cancer classification","volume":"1750","author":"Yasiran","year":"2016","journal-title":"AIP Conf. Proc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1038\/s41571-019-0177-5","article-title":"Challenges to curing primary brain tumours","volume":"16","author":"Aldape","year":"2019","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Yang, G., Lin, Y., Pang, H., and Wang, M. (2018). Automated glioma detection and segmentation using graphical models. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0200745"},{"key":"ref_5","unstructured":"Birry, R.A.K. (2013). Automated Classification in Digital Images of Osteogenic Differentiated Stem Cells. [Ph.D. Thesis, University of Salford]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Drevelegas, A., and Papanikolaou, N. (2011). Imaging modalities in brain tumors. Imaging of Brain Tumors with Histological Correlations, Springer.","DOI":"10.1007\/978-3-540-87650-2"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.ncl.2008.09.015","article-title":"Neuroimaging in Neuro-Oncology","volume":"27","author":"Mechtler","year":"2009","journal-title":"Neurol. Clin."},{"key":"ref_8","first-page":"1","article-title":"Brain Tumors: Epidemiology and Current Trends in Treatment","volume":"1","author":"Strong","year":"2015","journal-title":"J. Brain Tumors Neurooncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s00234-011-0886-7","article-title":"Segmentation of multiple sclerosis lesions in MR images: A review","volume":"54","author":"Mortazavi","year":"2011","journal-title":"Neuroradiology"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rundo, L., Tangherloni, A., Militello, C., Gilardi, M.C., and Mauri, G. (2016, January 6\u20139). Multimodal medical image registration using Particle Swarm Optimization: A review. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece.","DOI":"10.1109\/SSCI.2016.7850261"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","article-title":"VoxelMorph: A Learning Framework for Deformable Medical Image Registration","volume":"38","author":"Balakrishnan","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","unstructured":"MY-MS.org (2020, October 01). MRI Basics. Available online: https:\/\/my-ms.org\/mri_basics.htm."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/1748-717X-5-5","article-title":"Comparison of T2 and FLAIR imaging for target delineation in high grade gliomas","volume":"5","author":"Stall","year":"2010","journal-title":"Radiat. Oncol."},{"key":"ref_14","unstructured":"Society, N.B.T. (2020, October 03). Quick Brain Tumor Facts. Available online: https:\/\/braintumor.org\/brain-tumor-information\/brain-tumor-facts\/."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rahimeto, S., Debelee, T., Yohannes, D., and Schwenker, F. (2019). Automatic pectoral muscle removal in mammograms. Evol. Syst.","DOI":"10.1007\/s12530-019-09310-8"},{"key":"ref_16","first-page":"1","article-title":"Classifier Based Breast Cancer Segmentation","volume":"47","author":"Kebede","year":"2020","journal-title":"J. Biomim. Biomater. Biomed. Eng."},{"key":"ref_17","unstructured":"Cui, S., Shen, X., and Lyu, Y. (2019). Automatic Segmentation of Brain Tumor Image Based on Region Growing with Co-constraint. International Conference on Multimedia Modeling, Proceedings of the MMM 2019: MultiMedia Modeling, Thessaloniki, Greece, 8\u201311 January 2019, Springer."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1002\/ima.22211","article-title":"Automated Brain Tumour Segmentation Techniques\u2014A Review","volume":"27","author":"Angulakshmi","year":"2017","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.cmpb.2019.04.016","article-title":"A novel framework for MR image segmentation and quantification by using MedGA","volume":"176","author":"Rundo","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"165760","DOI":"10.1016\/j.ijleo.2020.165760","article-title":"Particle swarm optimized texture based histogram equalization (PSOTHE) for MRI brain image enhancement","volume":"224","author":"Acharya","year":"2020","journal-title":"Optik"},{"key":"ref_21","first-page":"2020","article-title":"Brain tumor extraction using marker controlled watershed segmentation","volume":"3","author":"Pandav","year":"2014","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Salman, Y. (2006, January 17\u201318). Validation techniques for quantitative brain tumors measurements. Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China.","DOI":"10.1109\/IEMBS.2005.1616129"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sarathi, M.P., Ansari, M.G.A., Uher, V., Burget, R., and Dutta, M.K. (2013, January 2\u20134). Automated Brain Tumor segmentation using novel feature point detector and seeded region growing. Proceedings of the 2013 36th International Conference on Telecommunications and Signal Processing (TSP), Rome, Italy.","DOI":"10.1109\/TSP.2013.6614016"},{"key":"ref_24","first-page":"427","article-title":"Brain Tumor Segmentation of MRI Brain Images through FCM clustering and Seeded Region Growing Technique","volume":"10","author":"Thiruvenkadam","year":"2015","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ho, Y.L., Lin, W.Y., Tsai, C.L., Lee, C.C., and Lin, C.Y. (November, January 31). Automatic Brain Extraction for T1-Weighted Magnetic Resonance Images Using Region Growing. Proceedings of the 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan.","DOI":"10.1109\/BIBE.2016.42"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bauer, S., Nolte, L.P., and Reyes, M. (2011). Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-642-23626-6_44"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1002\/ima.22253","article-title":"NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique","volume":"28","author":"Rundo","year":"2017","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Debelee, T.G., Schwenker, F., Ibenthal, A., and Yohannes, D. (2019). Survey of deep learning in breast cancer image analysis. Evol. Syst.","DOI":"10.1007\/s12530-019-09297-2"},{"key":"ref_29","first-page":"79","article-title":"Classification of Mammograms Using Texture and CNN Based Extracted Features","volume":"42","author":"Debelee","year":"2019","journal-title":"J. Biomim. Biomater. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Debelee, T.G., Kebede, S.R., Schwenker, F., and Shewarega, Z.M. (2020). Deep Learning in Selected Cancers\u2019 Image Analysis\u2014A Survey. J. Imaging, 6.","DOI":"10.3390\/jimaging6110121"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"131","DOI":"10.4028\/www.scientific.net\/JERA.51.131","article-title":"Detection of Bacterial Wilt on Enset Crop Using Deep Learning Approach","volume":"51","author":"Afework","year":"2020","journal-title":"Int. J. Eng. Res. Afr."},{"key":"ref_32","first-page":"89","article-title":"Classification of Mammograms Using Convolutional Neural Network Based Feature Extraction","volume":"Volume 244","author":"Debelee","year":"2018","journal-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, Q., Yu, Z., Wang, Y., and Zheng, H. (2020). TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation. Sensors, 20.","DOI":"10.3390\/s20154203"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.neucom.2019.07.006","article-title":"USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets","volume":"365","author":"Rundo","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e245","DOI":"10.2196\/jmir.2930","article-title":"The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration","volume":"15","author":"Kistler","year":"2013","journal-title":"J. Med. Internet Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)","volume":"34","author":"Menze","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnins.2019.00144","article-title":"Supervised Brain Tumor Segmentation Based on Gradient and Context-Sensitive Features","volume":"13","author":"Zhao","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref_39","first-page":"725","article-title":"Developing an Approach to Brain MRI Image Preprocessing for Tumor Detection","volume":"1","author":"Reddy","year":"2014","journal-title":"Int. J. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1016\/j.neuroimage.2004.03.032","article-title":"A Hybrid Approach to the Skull Stripping Problem in MRI","volume":"22","author":"Dale","year":"2004","journal-title":"Neuroimage"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1002\/ima.22208","article-title":"Tumor Detection in T1, T2, FLAIR and MPR Brain Images Using a Combination of Optimization and Fuzzy Clustering Improved by Seed-Based Region Growing Algorithm","volume":"27","author":"Vishnuvarthanan","year":"2017","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1016\/j.procs.2020.03.295","article-title":"Brain Tumor Segmentation from MRI Images using Hybrid Convolutional Neural Networks","volume":"167","author":"Daimary","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Havaei, M., Dutil, F., Pal, C., Larochelle, H., and Jodoin, P.M. (2016). A Convolutional Neural Network Approach to Brain Tumor Segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer.","DOI":"10.1007\/978-3-319-30858-6_17"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pereira, S., Pinto, A., Alves, V., and Silva, C.A. (2016). Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer.","DOI":"10.1007\/978-3-319-30858-6_12"},{"key":"ref_45","unstructured":"Malmi, E., Parambath, S., Peyrat, J.M., Abinahed, J., and Chawla, S. (2015). CaBS: A Cascaded Brain Tumor Segmentation Approach. Proc. MICCAI Brain Tumor Segmentation (BRATS), 42\u201347. Available online: http:\/\/www2.imm.dtu.dk\/projects\/BRATS2012\/proceedingsBRATS2012.pdf."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Debelee, T.G., Schwenker, F., Rahimeto, S., and Yohannes, D. (2019). Evaluation of modified adaptive k-means segmentation algorithm. Comput. Vis. Media.","DOI":"10.1007\/s41095-019-0151-2"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Dong, H., Yang, G., Liu, F., Mo, Y., and Guo, Y. (2017). Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks. Communications in Computer and Information Science, Springer.","DOI":"10.1007\/978-3-319-60964-5_44"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/2\/22\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:18:26Z","timestamp":1760159906000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/2\/22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,1]]},"references-count":47,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["jimaging7020022"],"URL":"https:\/\/doi.org\/10.3390\/jimaging7020022","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,1]]}}}