Digital Image Processing Methods for Detecting Cancer Cells: A Review
DOI:
https://doi.org/10.61841/rrmjfn60Keywords:
Cancer Identification, CT scan, Digital image processing, Enhancement, Lung cancerAbstract
In recent years, image processing mechanisms have been widely used in several medical areas to improve early detection and treatment phases, in which the time factor is very important for the patient to discover the disease as quickly as possible, especially in various cancer tumours such as lung cancer. Lung cancer has attracted the attention of the scientific and sciatic communities in recent years due to its high prevalence combined with difficult diagnosis. Statistics show that lung cancer is the one that attacks the largest number of people in the world. Early detection of lung cancer is very critical to successful treatment. There are few methods available to identify cancer cells. Here two segmentation approaches, such as thresholding and watershed, are used to identify cancer cells and to find a better response to them. Cancer diagnosis also requires the application of radiological imaging. Digital image processing is also used to monitor the spread of cancer and the progress of treatment and to monitor cancer. Oncological imagery is becoming increasingly diverse and precise. The aim of digital image processing is to find the most appropriate treatment option for each patient. Imaging methods are often used in conjunction to provide sufficient information.
Downloads
References
[1] K. Clark et al., “The cancer imaging archive (TCIA): Maintaining and operating a public information repository,” J. Digit. Imaging, 2013.
[2] E. B. Garon et al., “Pembrolizumab for the treatment of non-small-cell lung cancer,” N. Engl. J. Med., 2015.
[3] A. Taibi and S. Vecchio, “Breast Imaging,” in Comprehensive Biomedical Physics: First Edition, 2014.
[4] T. National and C. Cancer, “Non-Small Cell Lung,” Cancer, 2011.
[5] F. Nasim, B. F. Sabath, and G. A. Eapen, “Lung Cancer,” Medical Clinics of North America. 2019.
[6] I. Sechopoulos, “A review of breast tomosynthesis. Part I. The image acquisition process,” Medical Physics. 2013.
[7] L. M. Warren et al., “The effect of image processing on the detection of cancers in digital mammography,” Am. J. Roentgenol., 2014.
[8] S. Robertson, H. Azizpour, K. Smith, and J. Hartman, “Digital image analysis in breast pathology—from image processing techniques to artificial intelligence,” Translational Research. 2018.
[9] G. Stålhammar et al., “Digital image analysis outperforms manual biomarker assessment in breast cancer,” Mod. Pathol., 2016.
[10] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA. Cancer J. Clin., 2018.
[11] A. Al Nahid and Y. Kong, “Involvement of Machine Learning for Breast Cancer Image Classification: A Survey,” Computational and Mathematical Methods in Medicine. 2017.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
