Brain Tumor Detection From CT Scan Images Using Watershed Segmentation Algorithm

Authors

  • Himanshu Pandey  Department of Electronics and Communication Engineering Galgotias College of Engineering and Technology Greater Noida, Uttar Pradesh, India
  • Rishabh Jaiswal  Department of Electronics and Communication Engineering Galgotias College of Engineering and Technology Greater Noida, Uttar Pradesh, India
  • Priyanshu Balmiki  Department of Electronics and Communication Engineering Galgotias College of Engineering and Technology Greater Noida, Uttar Pradesh, India
  • Mukesh Chauhan  Department of Electronics and Communication Engineering Galgotias College of Engineering and Technology Greater Noida, Uttar Pradesh, India

DOI:

https://doi.org//10.32628/IJSRST229580

Keywords:

Computed Tomography (CT), Digital Image Processing (DIP), Watershed Segmentation

Abstract

The field of medical imaging gains value by increasing the need for automatic, reliable, fast and effective diagnostics that can provide insight into the image better than the human eye. A brain tumor is the second leading cause of cancer-related deaths in men aged 20 to 39 and the fifth leading cause of cancer among women in the same age group. Diagnosis of a tumor is a very important part of its treatment. Images are obtained by Computed Tomography (CT) and are processed for medical and therapeutic purposes. This paper discusses Watershed algorithm that can inform the user of tumor details using basic image processing techniques. This process helps to determine the size, shape and shape of the tumor. It helps medical staff and the patient understand the seriousness of the tumor. The contour GUI of the tumor and its boundary can provide information to medical personnel by clicking the user selection buttons.

References

  1. Zhang, Y., L. Wu, and S. Wang, “Magnetic resonance brain image classification by an improved artificial bee colony algorithm,” Progress in Electromagnetics Research,  Evelin Sujji, Y.V.S. Lakshmi, G. Wiselin Jiji, “MRI Brain Image Segmentation based on Thresholding”: International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-3 Number-1 Issue-8 March2013
  2. K.Selvanayaki, Dr.P.Kalugasalam Intelligent Brain Tumor Tissue Segmentation From Magnetic Resonance Image (Mri) Using Meta Heuristic Algorithms Journal Of Global Research In Computer Science Volume 4, No. 2, February 2013
  3. S.Roy And S.K.Bandoyopadhyay, “Detection And Qualification Of Brain Tumor From Mri Of Brain And Symmetric Analysis”, International Journal Of Information And Communication Technology Research, Volume 2 No.6, June 2012.
  4. Igor Bisio, Alessandro Fedeli, Fabio Lavagetto, Matteo Pastorino, Andrea Randazzo, Andrea Sciarrone, Emanuele Tavanti (2017) “Mobile Smart Helmet for Brain Stroke Early Detection through Neural Network based Signals Analysis” in IEEE Global Communications Conference.
  5. Mohammed Sabbih Hamoud AlTamimi Ghazali Sulong, “Tumor Brain Detection Through MRI Images: A Review of Literature”, Journal of Theoretical and Applied Information Technology 20th April 2014.
  6. Rupali Mahajan, Dr. P. M. Mahajan (2016) “Survey on Diagnosis of Brain Haemorrhage by Using Artificial Neural Network” in DETECTED TUMOR “International Journal of Engineering Research Online” Vol 4 Issue 4.
  7. C.S.Ee, K.S.Sim, V.Teh, F.F.Ting “Estimation of Window Width Setting for CT Scan Brain Images Using Mean of Greyscale Level to Standard Deviation Ratio”.
  8. Chiun-Li Chin, Bing-Jhang Lin, Guei-Ru Wu, Tzu- Chieh Weng, Cheng-Shiun Yang, Rui-Cih Su, Yu- Jen Pan (2017) “An Automated Early Ischemic Stroke Detection System using CNN Deep Learning Algorithm” in Icast2017.
  9. Yanran Wang, Aggelos K Katsaggelos, Xue Wang, Todd B Parrish (2016) “A Deep Symmetry Convent for Stroke Lesion Segmentation” in IEEE International Conference on Image Processing 2016.
  10. S.Roy And S.K.Bandoyopadhyay, “Detection And Qualification Of Brain Tumor From Mri Of Brain And Symmetric Analysis”, International Journal Of Information And Communication Technology Research, Volume 2 No.6, June 2012.
  11. Igor Bisio, Alessandro Fedeli, Fabio Lavagetto, Matteo Pastorino, Andrea Randazzo, Andrea Sciarrone, Emanuele Tavanti (2017) “Mobile Smart Helmet for Brain Stroke Early Detection through Neural Network based Signals Analysis” in IEEE Global Communications Conference.
  12. Mohammed Sabbih Hamoud AlTamimi Ghazali Sulong, “Tumor Brain Detection Through MRI Images: A Review of Literature”, Journal of Theoretical and Applied Information Technology 20th April 2014.
  13. Rupali Mahajan, Dr. P. M. Mahajan (2016) “Survey on Diagnosis of Brain Haemorrhage by Using Artificial Neural Network” in DETECTED TUMOR “International Journal of Engineering Research Online” Vol 4 Issue 4.
  14. C.S.Ee, K.S.Sim, V.Teh, F.F.Ting “Estimation of Window Width Setting for CT Scan Brain Images Using Mean of Greyscale Level to Standard Deviation Ratio”.
  15. Chiun-Li Chin, Bing-Jhang Lin, Guei-Ru Wu, Tzu- Chieh Weng, Cheng-Shiun Yang, Rui-Cih Su, Yu- Jen Pan (2017) “An Automated Early Ischemic Stroke Detection System using CNN Deep Learning Algorithm” in Icast2017.
  16. Yanran Wang, Aggelos K Katsaggelos, Xue Wang, Todd B Parrish (2016) “A Deep Symmetry Convent for Stroke Lesion Segmentation” in IEEE International Conference on Image Processing 2016.
  17. Vishal R.Shelke, Rajesh A.Rajwade, Dr. Mayur Kulkarni (2013) “Intelligent Acute Brain Hemorrhage Diagnosis System” in “Proc. of Int. Conf. on Advances in Computer Science, AETACS” Elsevier page 522 – 528.

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Published

2022-11-30

Issue

Section

Research Articles

How to Cite

[1]
Himanshu Pandey, Rishabh Jaiswal, Priyanshu Balmiki, Mukesh Chauhan, " Brain Tumor Detection From CT Scan Images Using Watershed Segmentation Algorithm, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 6, pp.141-146, November-December-2022. Available at doi : https://doi.org/10.32628/IJSRST229580