Enhanced Segmentation Algorithms for Improving Acute Lymphocytic Leukemia Diagnosis from Blood Microscopic Images

Enhanced Segmentation Algorithms for Improving Acute Lymphocytic Leukemia Diagnosis from Blood Microscopic Images

  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-4
Year of Publication : 2023
Author : Saranya Vijayan, Radha Venkatachalam
DOI : 10.14445/22315381/IJETT-V71I4P241

How to Cite?

Saranya Vijayan, Radha Venkatachalam, "Enhanced Segmentation Algorithms for Improving Acute Lymphocytic Leukemia Diagnosis from Blood Microscopic Images, " International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 483-490, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I4P241

Abstract
The application of Digital Image Processing on medical images could greatly help doctors to identify the disease in an early phase before it starts spreading. In this research work, the segmentation steps needed to find out the leukemic blood cells are being discussed and elaborating some of the important segmentation steps have been carried out. The resultant images will also be given for visual analysis. Some of the important Segmentation steps associated with the leukemic blood cell images will be given with the results. The major aim of this research work is to detect malignant leukaemia at the earliest so that it would improve the chances of survival of the patients. This research work has combined two enhanced segmentation algorithms to carry out the segmentation process, and also it has been proved that it works well when compared with the conventional segmentation algorithms.

Keywords
Segmentation, Blood cell Images, Enhanced Algorithms.

References
[1] C. Vidhya et al., “Classification of Acute Lymphoblastic Leukemia in Blood Microscopic Images Using SVM,” International Conference on Engineering Trends and Science & Humanities (ICETSH), pp. 185–189, 2015.
[Google Scholar] [Publisher Link]
[2] Shivani, and Er. Harjeet Singh, "The Performance Analysis of Edge Detection Algorithms for Image Processing Based on Improved Canny Operator," International Journal of Computer Trends and Technology, vol. 68, no. 10, pp. 29-34, 2020.
[CrossRef] [Publisher Link]
[3] P. Viswanathan, “Fuzzy C Means Detection of Leukemia Based on Morphological Contour Segmentation,” Procedia Computer Science, vol. 58, pp. 84–90, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Safa Riyadh Waheed et al., “Multifocus Watermarking Approach Based on Discrete Cosine Transform,” Microscopy Research and Technique, vol. 79, no. 5, pp. 431–437, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Sitanaboina S L Parvathi, and Harikiran Jonnadula, "A Hybrid Semantic Model for MRI Kidney Object Segmentation with Stochastic Features and Edge Detection Techniques" International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 411-420, 2022.
[CrossRef] [Publisher Link]
[6] Fabio Scotti, “Automatic Morphological Analysis for Acute Leukemia Identification in Peripheral Blood Microscope Images,” CIMSA 2005 – IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, pp. 96-101, 2005.
[Google Scholar] [Publisher Link]
[7] Gandikota Gopi, and T.Suneetha Rani, “An Adaptive Hierarchical Clustering Algorithm for Segmenting Sentence level Text,” International Journal of Computer & organization Trends (IJCOT), vol. 4, no. 6, no. 23-27, 2014.
[CrossRef] [Publisher Link]
[8] Gurpreet Singh, Gaurav Bathla and SharanPreet Kaur, “Design of New Architecture to Detectleukemia Cancerfrom Medical Images,” International Journal of Applied Engineering Research, vol. 11, no. 10, pp. 7087–7094, 2016.
[Google Scholar] [Publisher Link]
[9] N.Pradeepa, A.Renuga Devi,and Saumiya Jose Thomas, "Survey of MRI Brain Image Segmentation Methods," SSRG International Journal of Electronics and Communication Engineering, vol. 2, no. 2, pp. 21-23, 2015.
[CrossRef] [Publisher Link]
[10] Jyoti Rawat et al., “Classification of Acute Lymphoblastic Leukaemia Using Hybrid Hierarchical Classifiers,” Multimedia Tools and Applications, vol. 76, no. 18, pp. 19057–19085, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Trupti A. Kulkarni-Joshi, and Dilip S. Bhosale, “A Fast Segmentation Scheme for Acute Lymphoblastic Leukemia Detection,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 4, no. 1, pp. 2278–8875, 2014.
[Google Scholar] [Publisher Link]
[12] P. Nageswari, S. Rajan, and K. Manivel, "Medical Image Segmentation Approaches: A Survey," SSRG International Journal of Electronics and Communication Engineering, vol. 7, no. 7, pp. 1-3, 2020.
[CrossRef] [Publisher Link]
[13] Jensen Wong Jing Lung et al., “Fuzzy Phoneme Classification Using Multi-Speaker Vocal Tract Length Normalization,” IETE Technical Review, vol. 31, no. 2, pp. 128–136, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Subrajeet Mohapatra, Dipti Patra, and Sanghamitra Satpathy, “An Ensemble Classifier System for Early Diagnosis of Acute Lymphoblastic Leukemia in Blood Smear Images,” Neural Computing & Applications, vol. 24, pp. 1887–1904, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Vivaram Veera Raghavulu, and Ande. Prasad, "Dynamic Object Segmentation Approach For Videos," SSRG International Journal of Computer Science and Engineering, vol. 7, no. 9, pp. 18-24, 2020.
[CrossRef] [Publisher Link]
[16] Khitikun Meethongjan et al., “An Intelligent Fused Approach for Face Recognition,” Journal of Intelligent Systems, vol. 22, no. 2, pp. 197–212, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Vanika Singhal, and Preety Singh, “Texture Features for the Detection of Acute Lymphoblastic Leukemia,” Proceeding of International Conference on ICT for Sustainable Development. pp. 535–543, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[18] P. R. Tabrizi, S. H. Rezatofighi, and M. J. Yazdanpanah, “Using PCA and LVQ Neural Network for Automatic Recognition of Five Types of White Blood Cells,” 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 5593-5596, 2010.
[CrossRef] [Google Scholar] [Publisher Link]