Detection of Cancer Cells in Human Blood Samples Using Microscopic Images
Keywords:
Blood cell, abnormal cell, Image processing, Image segmentation, Image enhancement, Thresholding techniques.Abstract
Blood testing is now widely regarded as one of the most important clinical examinations. A blood cell's characteristics (volume, shape, and color) might reveal vital information about a patient's health. On the other hand, inspection process takes time and requires a high level of expertise knowledge. As a result, automated medical diagnosis technologies are needed to help clinicians identify illnesses promptly and reliably. The main objective of normal blood segmentation is just to isolate defective/abnormal units from a complex background and segments them into architectural elements utilizing image processing techniques such as contrast enhancement, thresholding, and morphological procedures, among others. The suggested method here reduces noise and enhances visual segmentation. All previous methods used different segmentation strategies, which resulted in less efficiency than the suggested method. This work can be implemented using MATLAB environment.
References
-
li>Ritika, Sandeep Kaur, Contrast Enhancement Techniques for Images– A Visual Analysis, International Journal of Computer Applications (0975– 8887), Volume 64– No.17, February 2013
- R., Adollah, M.Y., Mashor, N.F.M, Nasir, H., Rosline, H., Mahsin, H., Adilah, Blood Cell Image Segmentation: A Review‖, Biomed2008, Proceedings 21, 2008, pp. 141-144.
- N., Ritter, J., Cooper, Segmentation and Border Identification of Cells in Images of Peripheral Blood Smear Slides‖, 30thAustralasian Computer Science Conference, Conference in Research and Practice in Information Technology, Vol. 62, 2007, pp. 161-169.
- D.M.U., Sabino, L.D.F., Costa, L.D.F., E.G., Rizzatti, M.A., Zago, A Texture Approach to Leukocyte Recognition‖, Real Time Imaging, Vol. 10, 2004, pp. 205-206.
- Santhosh Krishna B.V., Jijin Godwin J., Tharanee Shree S., Sreenidhi B., Abinaya T. Detection of Leukemia and Its Types Using Combination of Support Vector Machine and K-Nearest Neighbors Algorithm‖ in Lecture Notes in Networks and SystemsThomas B., Harshitha R., Rafega Beham A. A novel approach to detect acute lymphoblastic leukemia‖ in 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018 – Proceedings.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRST

This work is licensed under a Creative Commons Attribution 4.0 International License.