Volume 9 • Issue 1 • PP: 31-44 • 2025
Classification Segmentation and Visualization of Intracranial Hemorrhage in CT Brain Images
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Intracranial hemorrhage (ICH) poses a large chance to affected person fitness, regularly modern requiring set off diagnosis and intervention. In latest years, the medical imaging techniques, specifically computed tomography (CT) scanning, have end up critical tools for detecting and characterizing ICH. This paper offers a complete evaluate comprehensive review of the state-of-the-art techniques for the segmentation, category, and visualization cutting-edge intracranial hemorrhage in CT mind pics. The evaluate encompasses numerous methodologies, consisting of conventional picture processing strategies, system cutting-edge algorithms, and deep brand new strategies, highlighting their strengths, limitations, and capability applications in scientific exercise. Additionally, it discusses the challenges associated with correct ICH detection and quantification, inclusive of the presence modern day artifacts, anatomical variations, and sophistication imbalance. Furthermore, the paper explores emerging tendencies in ICH research, which includes the combination trendy multimodal imaging information and the improvement trendy interactive visualization gear for enhanced medical choice-making. The segmented portion from each CT image is constructed into a single 3D volumetric structure and essential information such as region Area, volume and location are provided. Further the classification accuracy between normal brain and ICH brain is 95.8%. Such a 3D visualization, Classification and volumetric analysis of ICH can provide the exact and necessary information to the neurologist which is essential for the treatment of ICH.
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References
[1] Amelia, C., Adi, K. and Widodo, C.E., (2017),’Calculation of the cerebral haemorrhage volume using analysis of computed tomography image’, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349- 2163 Issue 06, Volume 4.
[2] Chan, T., (2007), ‘Computer aided detection of small acute intracranial haemorrhage on computer tomography of brain’, Computerized Medical Imaging and Graphics, pp.285-298.
[3] Dhawan, A.P., Loncaric, S., Hitt, K., Broderick, J. and Brott, T., (2007), ‘Image analysis and 3-D visualization of intracerebral brain haemorrhage’, In [2007] Computer-Based Medical Systems-Proceedings of the Sixth Annual IEEE Symposium, pp. 140-145.
[4] Guo, D., Wei, H., Zhao, P., Pan, Y., Yang, H.Y., Wang, X., Bai, J., Cao, K., Song, Q., Xia, J. and Gao, F., (2020), ‘Simultaneous Classification and Segmentation of Intracranial Haemorrhage Using a Fully Convolutional Neural Network’,IEEE 17th International Symposium on Biomedical Imaging (ISBI) , pp. 118-121.
[5] Hu, K., Chen, K., He, X., Zhang, Y., Chen, Z., Li, X. and Gao, X., (2020), ‘Automatic segmentation of intracerebral haemorrhage in CT images using encoder–decoder convolutional neural network’, Information Processing & Management, 57(6), pp.102352.
[6] Lee, T.H., Fauzi, M.F.A. and Komiya, R., (2009), ‘Segmentation of CT brain images using unsupervised clusterings’, Journal of visualization, pp.131-138.
[7] Ma, G.Q., Wang, X.J. and Li, X.L., (2013),’Optimization of k-means clustering Segmentation in Head CT images’, In Applied Mechanics and Materials ,Vol. 411, pp. 1247-1250.
[8] Majumdar, A., Brattain, L., Telfer, B., Farris, C. and Scalera, J., (2018), July, ‘Detecting intracranial haemorrhage with deep learning’, In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ,pp. 583-587.
[9] Patel, A., Van De Leemput, S.C., Prokop, M., Van Ginneken, B. and Manniesing, R., (2019), ‘Image Level Training and Prediction: Intracranial Haemorrhage Identification in 3D Non-Contrast CT’, IEEE Access, 7, pp.92355-92364.
[10] Qiu, Y., Chang, C.S., Yan, J.L., Ko, L. and Chang, T.S., (2019), October, ‘Semantic Segmentation of Intracranial Haemorrhages in Head CT Scans’, IEEE 10th International Conference on Software Engineering and Service Science(ICSESS),pp. 112-115.
[11] Rahmany, I. and Khlifa, N., (2014),’Detection of intracranial aneurysm in angiographic images using fuzzy approaches’,In International Image Processing, Applications and Systems Conference, pp. 1-6.
[12] Sherekar, R.M. and Pawar, A., (2014), ‘A Matlab image processing approach for reconstruction of DICOM images for manufacturing of customized anatomical implants by using rapid prototyping’, American Journal of Mechanical Engineering and Automation, 1(5), pp.48.
[13] Singh, P., Khanna, V. and Kamal, M., (2018), ‘Haemorrhage segmentation by fuzzy c-mean with Modified Level Set on CT imaging’, In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 550-555.
[14] Sumijan, M.S., Harlan, J. and Wibowo, E.P., (2017),’Hybrids Otsu method, feature region and mathematical morphology for calculating volume haemorrhage brain onCT- scan image and 3D reconstruction’, TELKOMNIKA Telecommunication, Computing, Electronics and Control, 15(1), pp.283-291.
[15] Kumarganesh S and Suganthi. M, (2018), “An Enhanced Medical Diagnosis Sustainable System for Brain Tumor Detection and Segmentation using ANFIS Classifier” Current Medical Imaging (Formerly as Current Medical Imaging Reviews), Volume 14, Number 2, pp. 271-279(9).
[16] B. V. Prakash, A. Rajiv Kannan, N. Santhiyakumari, S. Kumarganesh, et al. (2023), “Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform” Scientific Reports, 13 (14522), pp. 1-13. https://doi.org/10.1038/s41598-023-41576-6.
[17] Kumarganesh. S and M. Suganthi, (2016), “An Efficient Approach for Brain Image (Tissue) Compression Based on the Position of the Brain Tumor” International Journal of Imaging Systems and technology, Volume 26, Issue 4, Pages 237– 242.
[18] S. Kumarganesh, M. Suganthi, 2014, “Efficient Lossless Medical Image Compression Technique for Real World Applications Using JOSE-Encoding” International Journal of Applied Engineering Research, ISSN 0973-4562 Volume 9, (24) pp. 24625-24640.
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