A Review of Image Segmentation Methods in Brain Tumor
DOI:
https://doi.org/10.29304/jqcm.2022.14.3.981Keywords:
Image segmentation, Brain tumor, CT scan image, MRIAbstract
Accurate segmentation of the medical image of the brain is significant stage in the identification of a brain tumor during the preparation of radiotherapy. In general, medical images are utilized as radiographic techniques in diagnosis, clinical studies, and therapy planning, Segmentation is one of the most widely used methods to correctly classify the pixels in an image , This review sheet discusses a comprehensive literature review of modern methods of brain tumor segmentation, and outlines the extent and robustness of each currently existing method for brain tumor clinical image segmentation.
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References
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[25] Balafar, M. A. ,”Fuzzy C-mean based brain MRI segmentation algorithms.”, Artificial intelligence review, (2014) ,41(3), 441-449.
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[27] Latif, G., Alghazo, J., Sibai, F. N., Iskandar, D. A., & Khan, A. H. ,”Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation.”, Current medical imaging, (2021) ,17(8), 917-930
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[31] Vijay, J., & Subhashini, J. ,”An efficient brain tumor detection methodology using K-means clustering algoriftnn.”, International conference on communication and signal processing, (2013), (pp. 653-657). IEEE.
[32] Khan, A. R., Khan, S., Harouni, M., Abbasi, R., Iqbal, S., & Mehmood, Z. ,”Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification.”, Microscopy Research and Technique, (2021) ,84(7), 1389-1399.
[33] Arunkumar, N., Mohammed, M. A., Abd Ghani, M. K., Ibrahim, D. A., Abdulhay, E., Ramirez-Gonzalez, G., & de Albuquerque, V. H. C. ,”K-means clustering and neural network for object detecting and identifying abnormality of brain tumor.”, Soft Computing, (2019) ,23(19), 9083-9096.
[34] Muhammad, M., Zeebaree, D., Brifcani, A. M. A., Saeed, J., & Zebari, D. A. ,”Region of interest segmentation based on clustering techniques for breast cancer ultrasound images”, A review. Journal of Applied Science and Technology Trends, (2020) ,1(3), 78-91.
[35] Zhou, Y. M., Jiang, S. Y., & Yin, M. L. ,”A region-based image segmentation method with mean-shift clustering algorithm.”, In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, (2008) , (Vol. 2, pp. 366-370). IEEE.
[36] Karthick, S., Sathiyasekar, K., & Puraneeswari, A. ,”A survey based on region based segmentation.”, International Journal of Engineering Trends and Technology, (2014) ,7(3), 143-147.
[37] Biratu, E. S., Schwenker, F., Debelee, T. G., Kebede, S. R., Negera, W. G., & Molla, H. T. ,”Enhanced region growing for brain tumor MR image segmentation.”, Journal of Imaging, (2021) , 7(2), 22.
[38] Latif, G., Iskandar, D. A., & Alghazo, J. ,”Multiclass brain tumor classification using region growing based tumor segmentation and ensemble wavelet features.”, International Conference on Computing and Big Data (2018), (pp. 67-72).
[39] Reddy, C. K. K., Anisha, P. R., & Raju, G. V. S. ,”A novel approach for detecting the tumor size and bone cancer stage using region growing algorithm.”, International Conference on Computational Intelligence and Communication Networks (CICN), (2015), (pp. 228-233). IEEE.
[40] Węgliński, T., & Fabijańska, A. ,”Brain tumor segmentation from MRI data sets using region growing approach.”, In Perspective Technologies and Methods in MEMS Design , (2011), (pp. 185-188). IEEE.
[41] Al-Tamimi, M. S. H., & Sulong, G. ,”TUMOR BRAIN DETECTION THROUGH MR IMAGES”, A REVIEW OF LITERATURE. Journal of Theoretical & Applied Information Technology, (2014) , 62(2).
[42] Selvaraj, D., & Dhanasekaran, R. ,”Mri brain image segmentation techniques-A review.”, Indian Journal of Computer Science and Engineering (IJCSE), (2013) , 4(5), 0976-5166.
[43] Dhage, P., Phegade, M. R., & Shah, S. K. ,”Watershed segmentation brain tumor detection.”, International Conference on Pervasive Computing (ICPC), (2015) , (pp. 1-5). IEEE.
[44] Shanthakumar, P., & Ganesh Kumar, P. ,”Computer aided brain tumor detection system using watershed segmentation techniques.”, International Journal of Imaging Systems and Technology, (2015) , 25(4), 297-301.
[45] Singhai, P. P., & Ladhake, S. A. ,”Brain tumor detection using marker based watershed segmentation from digital mr images.”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN, (2013), 2278-3075.
[46] Sharma, A. K., Nandal, A., Dhaka, A., Koundal, D., Bogatinoska, D. C., & Alyami, H. ,”Enhanced watershed segmentation algorithm-based modified ResNet50 model for brain tumor detection.”, BioMed Research International, (2022).
[2] Wadhwa, A., Bhardwaj, A., & Verma, V. S.,” A review on brain tumor segmentation of MRI images”. Magnetic resonance imaging, (2019), 61, 247-259.
[3] Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. Image segmentation using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence(2021)..
[4] Kaur, G., & Rani, J.,” MRI brain tumor segmentation methods-a review.” (2016).
[5] Wang, D., Li, H., Wei, X., & Wang, X. P. ,”An efficient iterative thresholding method for image segmentation”, Journal of Computational Physics, (2017) , 350, 657-667.
[6] Feng, Y., Zhao, H., Li, X., Zhang, X., & Li, H. (2017),”A multi-scale 3D Otsu thresholding algorithm for medical image segmentation,” Digital Signal Processing, (2017) , 60, 186-199.
[7] Arifin, A. Z., & Asano, “A. Image segmentation by histogram thresholding using hierarchical cluster analysis.” , Pattern recognition letters, (2006) , 27(13), 1515-1521
[8] Al-Amri, S. S., & Kalyankar, N. V.,”Image segmentation by using threshold techniques”, arXiv preprint , (2010) , arXiv:1005.4020.
[9] Chen, J., Guan, B., Wang, H., Zhang, X., Tang, Y., & Hu, W. ,” Image thresholding segmentation based on two dimensional histogram using gray level and local entropy information”, IEEE Access, (2017) , 6, 5269-5275.
[10] Chabrier, S., Rosenberger, C., Laurent, H., Emile, B., & Marché, P. “Evaluating the segmentation result of a gray-level image”,. In 2004 12th European signal processing conference(2004) , (pp. 953-956). IEEE.
[11] Yang, W., Cai, L., & Wu, F. ,”Image segmentation based on gray level and local relative entropy two dimensional histogram.”, Plos one, (2020)., 15(3), e0229651.
[12] Liu, D., & Yu, J. ,”Otsu method and K-means.”, Ninth International Conference on Hybrid Intelligent Systems, (2009) ,(Vol. 1, pp. 344-349). IEEE.
[13] Lee, L. K., Liew, S. C., & Thong, W. J. ,” A review of image segmentation methodologies in medical image.”, Advanced computer and communication engineering technology, (2015), 1069-1080.
[14] Xu, X., Xu, S., Jin, L., & Song, E.,” Characteristic analysis of Otsu threshold and its applications”,. Pattern recognition letters, 32(7), (2011) , 956-961.
[15] Husham, S., Mustapha, A., Mostafa, S. A., Al-Obaidi, M. K., Mohammed, M. A., Abdulmaged, A. I., & George, S. T.,”Comparative analysis between active contour and otsu thresholding segmentation algorithms in segmenting brain tumor magnetic resonance imaging.”, Journal of Information Technology Management, 12(Special Issue: Deep Learning for Visual Information Analytics and Management.), (2020) ,48-61
[16] Pravitasari, A. A., Qonita, S. F., Irhamah, N. I., Fithriasari, K., Purnami, S. W., & Ferriastuti, W. ,”MRI-based brain tumor segmentation using gaussian and hybrid gaussian mixture model-spatially variant finite mixture model with expectation-maximization algorithm.”, Malaysian Journal of Mathematical Sciences, (2020),14(1), 77-93.
[17] Nguyen, D. M., Vu, H. T., Ung, H. Q., & Nguyen, B. T. ,”3D-brain segmentation using deep neural network and Gaussian mixture model.”, In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) , (2017), (pp. 815-824). IEEE.
[18] Chaddad, A. ,”Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models”, International Journal of Biomedical Imaging, (2015).
[19] Zaknich, A. ,”Principles of adaptive filters and self-learning systems.”, Springer Science & Business Media, (2005).
[20] Agn, M., af Rosenschöld, P. M., Puonti, O., Lundemann, M. J., Mancini, L., Papadaki, A., ... & Van Leemput, K. ,”A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.”, Medical image analysis, (2019), 54, 220-237.
[21] Elizabeth, J. R., & Juliet, S. E. ,”A survey on various segmentation methods in medical imaging.”, International Journal of Emerging Trends in Engineering Research, (2019),7(11), 1-5.
[22] Madhulatha, T. S. ,”An overview on clustering methods”,. arXiv preprint, (2012), arXiv:1205.1117.
[23] Naik, D., & Shah, P. ,”A review on image segmentation clustering algorithms.”, Int J Comput Sci Inform Technol , (2014) , 5(3), 3289-93.
[24] Selvy, P. T., Palanisamy, V., & Purusothaman, T. ,”Performance analysis of clustering algorithms in brain tumor detection of MR images.”, European Journal of Scientific Research, (2011) ,62(3), 321-330.
[25] Balafar, M. A. ,”Fuzzy C-mean based brain MRI segmentation algorithms.”, Artificial intelligence review, (2014) ,41(3), 441-449.
[26] Patil, M., Pawar, M., Patil, M., & Nichal, A. ,”A review paper on brain tumor segmentation and detection”. IJIREEICE, . (2017) ,5(1), 12-15.
[27] Latif, G., Alghazo, J., Sibai, F. N., Iskandar, D. A., & Khan, A. H. ,”Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation.”, Current medical imaging, (2021) ,17(8), 917-930
[28] Arora, J., Khatter, K., & Tushir, M. ,”Fuzzy c-means clustering strategies: A review of distance measures.,” Software Engineering, (2019), 153-162.
[29] Alam, M. S., Rahman, M. M., Hossain, M. A., Islam, M. K., Ahmed, K. M., Ahmed, K. T., ... & Miah, M. S. ,”Automatic human brain tumor detection in MRI image using template-based K means and improved fuzzy C means clustering algorithm.”, Big Data and Cognitive Computing,(2019), 3(2), 27.
[30] Wu, M. N., Lin, C. C., & Chang, C. C. ,”Brain tumor detection using color-based k-means clustering segmentation.”, In Third international conference on intelligent information hiding and multimedia signal processing (IIH-MSP 2007) ,(Vol. 2, pp. 245-250). IEEE.
[31] Vijay, J., & Subhashini, J. ,”An efficient brain tumor detection methodology using K-means clustering algoriftnn.”, International conference on communication and signal processing, (2013), (pp. 653-657). IEEE.
[32] Khan, A. R., Khan, S., Harouni, M., Abbasi, R., Iqbal, S., & Mehmood, Z. ,”Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification.”, Microscopy Research and Technique, (2021) ,84(7), 1389-1399.
[33] Arunkumar, N., Mohammed, M. A., Abd Ghani, M. K., Ibrahim, D. A., Abdulhay, E., Ramirez-Gonzalez, G., & de Albuquerque, V. H. C. ,”K-means clustering and neural network for object detecting and identifying abnormality of brain tumor.”, Soft Computing, (2019) ,23(19), 9083-9096.
[34] Muhammad, M., Zeebaree, D., Brifcani, A. M. A., Saeed, J., & Zebari, D. A. ,”Region of interest segmentation based on clustering techniques for breast cancer ultrasound images”, A review. Journal of Applied Science and Technology Trends, (2020) ,1(3), 78-91.
[35] Zhou, Y. M., Jiang, S. Y., & Yin, M. L. ,”A region-based image segmentation method with mean-shift clustering algorithm.”, In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, (2008) , (Vol. 2, pp. 366-370). IEEE.
[36] Karthick, S., Sathiyasekar, K., & Puraneeswari, A. ,”A survey based on region based segmentation.”, International Journal of Engineering Trends and Technology, (2014) ,7(3), 143-147.
[37] Biratu, E. S., Schwenker, F., Debelee, T. G., Kebede, S. R., Negera, W. G., & Molla, H. T. ,”Enhanced region growing for brain tumor MR image segmentation.”, Journal of Imaging, (2021) , 7(2), 22.
[38] Latif, G., Iskandar, D. A., & Alghazo, J. ,”Multiclass brain tumor classification using region growing based tumor segmentation and ensemble wavelet features.”, International Conference on Computing and Big Data (2018), (pp. 67-72).
[39] Reddy, C. K. K., Anisha, P. R., & Raju, G. V. S. ,”A novel approach for detecting the tumor size and bone cancer stage using region growing algorithm.”, International Conference on Computational Intelligence and Communication Networks (CICN), (2015), (pp. 228-233). IEEE.
[40] Węgliński, T., & Fabijańska, A. ,”Brain tumor segmentation from MRI data sets using region growing approach.”, In Perspective Technologies and Methods in MEMS Design , (2011), (pp. 185-188). IEEE.
[41] Al-Tamimi, M. S. H., & Sulong, G. ,”TUMOR BRAIN DETECTION THROUGH MR IMAGES”, A REVIEW OF LITERATURE. Journal of Theoretical & Applied Information Technology, (2014) , 62(2).
[42] Selvaraj, D., & Dhanasekaran, R. ,”Mri brain image segmentation techniques-A review.”, Indian Journal of Computer Science and Engineering (IJCSE), (2013) , 4(5), 0976-5166.
[43] Dhage, P., Phegade, M. R., & Shah, S. K. ,”Watershed segmentation brain tumor detection.”, International Conference on Pervasive Computing (ICPC), (2015) , (pp. 1-5). IEEE.
[44] Shanthakumar, P., & Ganesh Kumar, P. ,”Computer aided brain tumor detection system using watershed segmentation techniques.”, International Journal of Imaging Systems and Technology, (2015) , 25(4), 297-301.
[45] Singhai, P. P., & Ladhake, S. A. ,”Brain tumor detection using marker based watershed segmentation from digital mr images.”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN, (2013), 2278-3075.
[46] Sharma, A. K., Nandal, A., Dhaka, A., Koundal, D., Bogatinoska, D. C., & Alyami, H. ,”Enhanced watershed segmentation algorithm-based modified ResNet50 model for brain tumor detection.”, BioMed Research International, (2022).
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Published
2022-08-12
How to Cite
Mutlaq, M. A., & Abdulbaqia, H. S. (2022). A Review of Image Segmentation Methods in Brain Tumor. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(3), Comp Page 1–9. https://doi.org/10.29304/jqcm.2022.14.3.981
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