FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-100403

Research Article |

Oral Tumor Segmentation and Detection using Clustering and Morphological Process

Author(s): Mahima Bhopal1, Rajeev Ranjan2 and Ashutosh Tripathi3

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 4

Publisher : FOREX Publication

Published : 18 October 2022

e-ISSN : 2347-470X

Page(s) : 788-791

DOI: https://doi.org/10.37391/IJEER.100403

Abstract

Oral tumor is one of the most widely recognized tumors growing globally, continuously promoting a high mortality rate. Because early detection and treatment remain the most effective interventions in improving oral cancer outcomes, developing complementary vision-based technologies that can reveal potential evil high-quality oral diseases (OPMDs), which carry the risk of developing cancer, represent significant opportunities for the oral screening process. This paper proposes a morphological algorithm to preserve edge details and prominent features in dental radiographs. This technique, in the early stage identifies the oral tumor detection using clustering and morphological processing. This algorithm would allow for the identification of tumors in these images. Applying pre-processing in images leads to over-segmentation even though it is pre-processed.

Keywords: Oral tumor, Segmentation, Clustering, Morphological processing




Mahima Bhopal*, Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab, India; Email: mahima30bhopl@gmail.com

Rajeev Ranjan, Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab, India; Email: rajeevranjan1134@gmail.com

Ashutosh Tripathi *, Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab, India; Email: ashu20034@gmail.com

    [1] Ranjan, R., Jindal, N., & Singh, A. K., “The identities of n-dimensional s-transform and applications,” Multimedia Tools and Applications, 2022. https://doi.org/10.1007/s11042-022-12757-8[Cross Ref]
    [2] B. Song et al., “Mobile-based oral cancer classification for point-of-care screening,” Journal of Biomedical Optics, vol. 26, no. 6, 2021. https://doi.org/10.1117/1.jbo.26.6.065003. [Cross Ref]
    [3] Sharma, N., Nawn, D., Pratiher, S., Shome, S., Chatterjee, R., Biswas, K., Pal, M., Paul, R. R., Dutta, S., & Chatterjee, J. (2021). Multifractal Texture Analysis of Salivary Fern Pattern for Oral Pre-Cancers and Cancer Assessment. IEEE Sensors Journal, 21(7), 9333–9340 https://doi.org/10.1109/JSEN.2021.3053262[Cross Ref]
    [4] C. S. et al., “Machine learning and treatment outcome prediction for oral cancer,” Journal of Oral Pathology and Medicine, vol. 49, no. 10, pp. 977–985, 2020. https://doi.org/10.1111/jop.13089.[Cross Ref]
    [5] Ranjan, R., Jindal, N., & Singh, A. K. (2020). Fractional S-Transform and Its Properties: A Comprehensive Survey. In Wireless Personal Communications (Vol. 113, Issue 4, pp. 2519–2541). Springer. https://doi.org/10.1007/s11277-020-07339-6.[Cross Ref]
    [6] R. A. Welikala, et al., “Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer,” IEEE Access, 8, 132677–132693, 2020. https://doi.org/10.1109/ACCESS.2020.3010180.[Cross Ref]
    [7] R. Ranjan, N. Jindal, and A.K. Singh, “A Sampling Theorem for Fractional S- transform with Error Estimation, “Digital signal processing, vol. 93 (2019), pp.138-150.[Cross Ref]
    [8] R. Ranjan, N. Jindal, and A.K. Singh, “Convolution theorem with its derivatives and multiresolution analysis for fractional S-transform,” Circuits, systems, and signal process, (2019) 1-24. [Cross Ref]
    [9] R. Ranjan, N. Jindal, and A.K. Singh, “A sampling theorem with error estimation for S-transform,” Integral Transforms and Special Functions, (2019) 1-21. [Cross Ref]
    [10] Chan, C. H., Huang, T. T., Chen, C. Y., Lee, C. C., Chan, M. Y., & Chung, P. C. (2019). Texture-Map-Based Branch-Collaborative Network for Oral Cancer Detection. IEEE Transactions on Biomedical Circuits and Systems, 13(4), 766–780. https://doi.org/10.1109/TBCAS.2019.2918244.[Cross Ref]
    [11] H. Zhalong et al., Early-Stage Oral Cavity Cancer Detection: Anisotropic Pre-Processing and Fuzzy C-Means Segmentation, 2018.[Cross Ref]
    [12] R. Ranjan, N. Jindal, and A.K. Singh, “Multiplicative filter design using S-transform,” IEEE conference ‘ICMETE-2018’ DOI: 10.1109/ICMETE.2018.00064.[Cross Ref]
    [13] T. Derlin, et al., “Molecular Imaging in Oncology Using Positron Emission Tomography,” Deutsches Arzteblatt international, vol. 115, no. 11, pp.175–181, 2018. [Cross Ref]
    [14] A. E. Heidari et al., “Optical Coherence Tomography as an Oral Cancer Screening Adjunct in a Low Resource Settings,” IEEE Journal of Selected Topics in Quantum Electronics, 2018. [Cross Ref]
    [15] J. Wang, Y. Xu, and S. A. Boppart, “Review of optical coherence tomography in oncology,” Journal of Biomedical Optics, vol. 22, no.12, 2017.[Cross Ref]
    [16] K. Anuradha and K. S. Narayanan, “Detection of Oral Tumor based on Marker-Controlled Watershed Algorithm,” In International Journal of Computer Applications, vol. 52, Issue 2, 2012.[Cross Ref]
    [17] Harendra singh and Roop Singh Solanki (2021), Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach. IJEER 9(2), 10-15. DOI: 10.37391/IJEER.090202. https://ijeer.forexjournal.co.in/archive/volume-9/ijeer-090202.html[Cross Ref]
    [18] Harendra Singh, Roop Singh, Parul Goel, Anil Singh and Naveen Sharma (2022), Automatic Framework for Vegetable Classification using Transfer-Learning. IJEER 10(2), 405-410. DOI: 10.37391/IJEER.100257.[Cross Ref]
    [19] V. Sanjay and P. Swarnalatha (2022), A Survey on Various Machine Learning Techniques for an Efficient Brain Tumor Detection from MRI Images. IJEER 10(2), 177-182. DOI: 10.37391/IJEER.100222.[Cross Ref]

Mahima Bhopal, Rajeev Ranjan and Ashutosh Tripathi (2022), Oral Tumor Segmentation and Detection using Clustering and Morphological Process. IJEER 10(4), 788-791. DOI: 10.37391/IJEER.100403.