Leukaemia’s Cells Pattern Tracking Via Multi-phases Edge Detection Techniques

Authors

  • Moath Ali Alshorman Institute of Engineering Mathematics, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia
  • Ahmad Kadri Junoh Institute of Engineering Mathematics, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia
  • Wan Zuki Azman Wan Muhamad Institute of Engineering Mathematics, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia
  • Mohd Hafiz Zakaria Institute of Engineering Mathematics, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia
  • Afifi Md Desa Institute of Engineering Mathematics, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia

Keywords:

Leukemia Edge Detection, Medical Image Processing, Pattern recognition, Ant Colony Optimization,

Abstract

Edge detection involves identifying and tracing the sudden sharp discontinuities to extract meaningful information from an image. The purpose of this paper is to improve detecting the leukaemia edges in the blood cell image. Toward this end, two distinctive procedures are developed which are Ant Colony Optimization Algorithm and the gradient edge detectors (Sobel, Prewitt and Robert). The latter involves image filtering, binarization, kernel convolution filtering and image transformation. Meanwhile, ACO involves filtering, enhancement, detection and localisation of the edges. Finally, the performance of the edge detection methods ACO, Sobel, Prewitt and Robert is compared to determine the best edge detection method. The results revealed that the Prewitt edge detection method produced an optimal performance for detecting edges of leukaemia cells with a value of 107%. Meanwhile, the ACO, Sobel and Robert yielded performance results of 76%, 102% and 93% respectively. Overall findings indicated that the gradient edge detection methods are superior to the Ant Colony Optimization method.

References

Liu, X., & Fang, S. (2015). A convenient and robust edge detection method based on ant colony optimization. Optics Communications, 353, 147–157.

Muthukrishnan, R., & Radha, M. (2011). Edge Detection Techniques for Image Segmentation. International Journal of

Pratt, W. K. (2001). Digital image processing (3rd ed.). New York, USA: John Wiley. Computer Science and Information Technology, 3(6), 259–267.

Fan, L., Song, F., & Jutamulia, S. (2007). Edge detection with large depth of focus using differential Haar-Gaussian wavelet transform. Optics Communications, 270(2), 169–175.

Lu, D.-S., & Chen, C.-C. (2008). Edge detection improvement by ant colony optimization. Pattern Recognition Letters, 29(4), 416–425.

Abood, D. Z. M. (2013). Edges enhancement of medical color images using add images. Journal of Research and Method in Education, 2(4), 52–60. Retrieved from

Chalana, V., & Kim, Y. (1997). A methodology for evaluation of boundary detection algorithms on medical images. IEEE Transactions on Medical Imaging, 16(5), 642–652.

Maulik, U. (2009). Medical image segmentation using genetic algorithms. IEEE Transactions on Information Technology in Biomedicine, 13(2), 166–173.

Nee, L. H., Mashor, M. Y., & Hassan, R. (2012). White blood cell segmentation for acute leukemia bone marrow images. Journal of Medical Imaging and Health Informatics, 2(3), 278–284.

Reta, C., Altamirano, L., Gonzalez, J. A., Diaz-Hernandez, R., Peregrina, H., Olmos, I., … Lobato, R. (2015). Segmentation and classification of bone marrow cells images using contextual information for medical diagnosis of acute leukemias. PLoS ONE, 10(6), 1–18.

Ramadevi, Y., Sridevi, T., Poornima, B., & Kalyani, B. (2010). Segmentation and object recognition using edge detection techniques. International Journal of Computer Science and Information Technology, 2(6), 153–161.

Poornima, B., Ramadevi, Y., & Sridevi, T. (2011). Threshold based edge detection algorithm. International Journal of Engineering and Technology, 3(4), 400.

Rajeswari, R., & Rajesh, R. (2011). A modified ant colony optimization based approach for image edge detection. In International Conference on Image Information Processing (ICIIP) (pp. 1–6). IEEE.

Mahaja, S., Golait, S. S., Meshram, A., & Jichlkan, N. (2014). Review: Detection of types of Acute leukemia. International Journal of Computer Science and Mobile Computing, 3(3), 104–111.

Chin Neoh, S., Srisukkham, W., Zhang, L., Todryk, S., Greystoke, B., Peng Lim, C., Aslam, N. (2015). An intelligent decision support system for leukaemia diagnosis using microscopic blood images. Nature Scientific Reports, 5, 14. 14938.

Mohapatra, S., Patra, D., & Satpathy, S. (2011). Automated leukemia detection in blood microscopic images using statistical texture analysis. International Conference Proceeding on Communication, Computing and Security (pp. 184–187). ACM.

Agaian, S., Madhukar, M., & Chronopoulos, A. T. (2014). Automated screening system for acute myelogenous leukemia detection in blood microscopic images. IEEE Systems Journal, 8(3), 995–1004.

Mohapatra, S., Samanta, S. S., Patra, D., & Satpathi, S. (2011). Fuzzy based blood image segmentation for automated leukemia detection. In International Conference on Devices and Communications (ICDeCom) (pp. 1–5). IEEE.

Reta, C., Robles, L. A., Gonzalez, J. A., Diaz, R., & Guichard, J. S. (2010). Segmentation of Bone Marrow Cell Images for a Computer Science, 58, 84–90.

Chatap, N., & Shibu, S. (2014). Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour. Journal of Computer Engineering, 16(5), 79–87.

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Published

2018-05-30

How to Cite

Alshorman, M. A., Junoh, A. K., Wan Muhamad, W. Z. A., Zakaria, M. H., & Md Desa, A. (2018). Leukaemia’s Cells Pattern Tracking Via Multi-phases Edge Detection Techniques. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-15), 33–37. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4042

Issue

Vol. 10 No. 1-15: Exploring Man-Machines Compliance in Robotics and Image Processing

Section

Articles

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