Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection
DOI:
https://doi.org/10.30812/matrik.v23i3.3619Keywords:
Brain Tumor, Computed Tomography-Scan, Improved Image, Segmentation, ThresholdAbstract
Diagnosing disease by playing the role of image processing is one form of current medical technology development. The results of image processing performance have been able to provide accurate diagnoses to be used as material for decision-making. This research aims to carry out the process of detecting brain tumor objects in Computed Tomography (CT-Scan) images by developing a segmentation technique using the Adaptive Threshold Morphology (ATM) algorithm. The performance of the ATM algorithm in the segmentation process involves the Extended Adaptive Global Treshold (eAGT) function to produce an optimal threshold value. This research method involves several stages of the process in detecting tumor objects. The preprocessing stage is carried out using the cropping and filtering process which is optimized using the eAGT function. The next stage is the morphological segmentation process involving erosion and dilation operations. The final stage of the segmentation process using the ATM algorithm is labeling objects that have been detected. The research dataset used 187 Computed Tomography-Scan images from 10 brain tumor patients. The results of this study show that the accuracy rate for detecting brain tumor objects in Computed Tomography-Scan images is 93.47%. These results can provide an automatic and effective detection process based on the optimal threshold value that has been generated. Overall, this research contributes to the development of segmentation algorithms in image processing and can be used as an alternative solution in the treatment of brain tumor patients.
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References
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[2] T. Sadad et al., “Brain tumor detection and multiâ€classification using advanced deep learning techniques,†Microsc. Res. Tech., vol. 84, no. 6, pp. 1296–1308, 2021, https://doi.org/10.1002/jemt.23688.
[3] A. Soni and A. Rai, “CT Scan Based Brain Tumor Recognition and Extraction using Prewitt and Morphological Dilation,†in 2021 International Conference on Computer Communication and Informatics (ICCCI), 2021, pp. 1–6, DOI: 10.1109/ICCCI50826.2021.9402677.
[4] R. Ranjbarzadeh, A. Caputo, E. B. Tirkolaee, S. Jafarzadeh Ghoushchi, and M. Bendechache, “Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools,†Comput. Biol. Med., vol. 152, p. 106405, 2023, doi: https://doi.org/10.1016/j.compbiomed.2022.106405.
[5] A. H. Khan et al., “Intelligent Model for Brain Tumor Identification Using Deep Learning,†Appl. Comput. Intell. Soft Comput., vol. 2022, p. 8104054, 2022,https://doi.org/10.1155/2022/8104054.
[6] A. Younis, L. Qiang, C. O. Nyatega, M. J. Adamu, and H. B. Kawuwa, “Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches,†Appl. Sci., vol. 12, no. 14, pp 1-20, 2022, https://doi.org/10.3390/app12147282.
[7] M. Arif, F. Ajesh, S. Shamsudheen, O. Geman, D. Izdrui, and D. Vicoveanu, “Brain tumor detection and classification by MRI using biologically inspired orthogonal wavelet transform and deep learning techniques,†J. Healthc. Eng., vol. 2022, no. 1, pp 1-18, 2022, https://doi.org/10.1155/2022/2693621.
[8] Y. Cheng, “Image segmentation technology and its application in digital image processing,†Proceedings of IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021. pp. 1174–1177, 2021, DOI: 10.1109/IPEC51340.2021.9421206.
[9] D. U. Palani, M. Vasanthi, and M. S. R. Begam, “Enhancement of medical image fusion using image processing,†J. Innov. Image Process., vol. 2, no. 4, pp. 165–174, 2020, https://doi.org/10.36548/jiip.2020.4.001.
[10] S. Ali, A. Tanveer, A. Hussain, and S. U. Rehman, “Identification of cancer disease using image processing approahes,†Int. J. Intell. Inf. Syst., vol. 9, no. 2, pp. 6–15, 2020, https://doi.org/10.11648/j.ijiis.20200902.11.
[11] M. Xu, L. Guo, and H.-C. Wu, “Novel Robust Automatic Brain-Tumor Detection and Segmentation Using Magnetic Resonance Imaging,†IEEE Sens. J., vol. 24, no. 7, pp. 10957–10964, 2024, DOI: 10.1109/JSEN.2024.3367123.
[12] A. K. Sharma, A. Nandal, A. Dhaka, D. Koundal, D. C. Bogatinoska, and H. Alyami, “[Retracted] Enhanced Watershed Segmentation Algorithmâ€Based Modified ResNet50 Model for Brain Tumor Detection,†Biomed Res. Int., vol. 2022, no. 1, pp 1-14, 2022, https://doi.org/10.1155/2022/7348344.
[13] F. Fakouri, M. Nikpour, and A. Soleymani Amiri, “Automatic Brain Tumor Detection in Brain MRI Images using Deep Learning Methods,†J. AI Data Min., vol. 12, no. 1, pp 27-35, 2024, https://doi.org/10.22044/jadm.2024.13148.2452.
[14] S. Gajula and V. Rajesh, “An MRI brain tumour detection using logistic regression-based machine learning model,†Int. J. Syst. Assur. Eng. Manag., vol. 15, no. 1, pp. 124–134, 2024, https://doi.org/10.1007/s13198-022-01680-8.
[15] P. Upadhyay, S. Saifi, J. Koul, R. Rani, P. Bansal, and A. Sharma, “Classification of Brain Tumors Using Augmented MRI Images and Deep Learning,†in 2024 2nd International Conference on Computer, Communication and Control (IC4), 2024, pp. 1–7, DOI: 10.1109/IC457434.2024.10486455.
[16] E. Michael, H. Ma, H. Li, F. Kulwa, and J. Li, “Breast cancer segmentation methods: current status and future potentials,†Biomed Res. Int., vol. 2021, no. 1, pp 1-29, 2021, https://doi.org/10.1155/2021/9962109.
[17] A. Kulshreshtha and A. Nagpal, “IFAS: improved fully automatic segmentation convolutional neural network model along with morphological segmentation for brain tumor detection,†Int. J. Inf. Technol., vol. 16, no. 3, pp. 1517–1525, 2024, https://doi.org/10.1007/s41870-023-01572-5.
[18] P. Saxena, A. Maheshwari, and S. Maheshwari, “Predictive modeling of brain tumor: a deep learning approach,†in Innovations in Computational Intelligence and Computer Vision: Proceedings of ICICV 2020, Springer, 2020, pp. 275–285, ISBN: 978-981-15-6067-5.
[19] A. Shanthini, G. Manogaran, G. Vadivu, K. Kottilingam, P. Nithyakani, and C. Fancy, “Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network,†J. Ambient Intell. Humaniz. Comput., pp. 1–15, 2021, https://doi.org/10.1007/s12652-021-02923-5.
[20] S. K. H. Seere and K. Karibasappa, “Threshold segmentation and watershed segmentation algorithm for brain tumor detection using support vector machine,†Eur. J. Eng. Technol. Res., vol. 5, no. 4, pp. 516–519, 2020, doi. 10.24018/ejeng.2020.5.4.1902.
[21] N. R. Shenoy and A. Jatti, “Ultrasound image segmentation through deep learning based improvised U-net,†Indones. J. Electr. Eng. Comput. Sci., vol. 21, no. 3, pp. 1424–1434, 2021, https://doi.org/10.11591/ijeecs.v21.i3.pp1424-1434.
[22] N. H. Ali, A. R. Abdullah, N. M. Saad, A. S. Muda, T. Sutikno, and M. H. Jopri, “Brain stroke computed tomography images analysis using image processing: A review,†IAES Int. J. Artif. Intell., vol. 10, no. 4, pp. 1048–1059, 2021, https://doi.org/10.11591/ijai.v10.i4.pp1048-1059.
[23] S. Arlis, S. Defit, and Sumijan, “Automated model for identification on mastoid of temporal bone image,†Indones. J. Electr. Eng. Comput. Sci., vol. 31, no. 1, pp. 570–581, 2023, https://doi.org/10.11591/ijeecs.v31.i1.pp570-581.
[24] M. Z. N. Al-Dabagh, “Automated tumor segmentation in MR brain image using fuzzy C-means clustering and seeded region methodology,†IAES Int. J. Artif. Intell., vol. 10, no. 2, pp. 284–290, 2021, https://doi.org/10.11591/ijai.v10.i2.pp284-290.
[25] M. N. R. Devi, A. Kumar, G. Swetha, U. S. Chavan, and V. M. Davasam, “Cancer Detection Using Image Processing and Machine Learning,†in 2022 International Conference on Artificial Intelligence and Data Engineering (AIDE), 2022, pp. 96–100, DOI: 10.1109/AIDE57180.2022.10059977.
[26] H. A. Saeed, S. Hamad, and A. T. Hussain, “Analysis the digital images by using morphology operators,†Indones. J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 1654–1662, 2021, https://doi.org/10.11591/ijeecs.v24.i3.pp1654-1662.
[27] R. Godse and S. Bhat, “Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line,†IEEE Access, vol. 8, pp. 38459–38471, 2020, DOI: 10.1109/ACCESS.2020.2975431.
[28] X. Luo et al., “A deep convolutional neural network for diabetic retinopathy detection via mining local and longâ€range dependence,†CAAI Trans. Intell. Technol., vol. 9, no. 1, pp. 153–166, 2024, https://doi.org/10.1049/cit2.12155.
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Published
2024-07-04
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How to Cite
[1]
S. Arlis, M. R. Putra, and M. Yanto, “Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection”, MATRIK, vol. 23, no. 3, pp. 653–662, Jul. 2024, doi: 10.30812/matrik.v23i3.3619.
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