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Image Processing Approach for Segmentation of WBC Nuclei based on K-Means Clustering

Published: 04 June 2021 Publication History

Abstract

Pathologists do manual microscopic assessment to check for abnormality which is very time consuming process. Automating blood cells classification is a requirement to aid them for robust and accurate results. White blood cells are used to diagnose some particular diseases like Leukemia, Immune Deficiency Syndrome and many others. They help in monitoring the fitness of a person. Segmentation is the foremost step in automatic blood cell classification which is a perplexing task because of alterations in appearance of cells under microscope. In this paper, we have presented a successful technique for automated white blood cells’ nuclei segmentation by keeping in view the fact of variation in light intensity. We have combined the contrast adjustment, K-means clustering and threshold (to remove false objects) to segment nuclei from blood smear image. This technique is tested on all five types of white blood cells to compare relative performance. The results of the proposed method showed that the Dice score, Sensitivity, False Positive Rate (FPR) and Accuracy are 97.81%, 98.29%, 0.14% and 99.78%, respectively for nuclei segmentation.

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  • (2024)Segmentation and classification of white blood SMEAR images using modified CNN architectureDiscover Applied Sciences10.1007/s42452-024-06139-y6:11Online publication date: 2-Nov-2024
  • (2023)Chaotic fitness-dependent quasi-reflected Aquila optimizer for superpixel based white blood cell segmentationNeural Computing and Applications10.1007/s00521-023-08486-035:21(15315-15332)Online publication date: 9-Apr-2023
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cover image ACM Other conferences
ICIGP '21: Proceedings of the 2021 4th International Conference on Image and Graphics Processing
January 2021
231 pages
ISBN:9781450389105
DOI:10.1145/3447587
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 04 June 2021

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Author Tags

  1. Blood Smear Analysis
  2. Contrast Adjustment
  3. K-Means Clustering
  4. Microscopic Image Processing
  5. Nuclei Segmentation
  6. White Blood Cells

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Cited By

View all
  • (2024)Adam golden search optimization enabled DCNN for classification of breast cancer using histopathological imageBiomedical Signal Processing and Control10.1016/j.bspc.2024.10623994(106239)Online publication date: Aug-2024
  • (2024)Segmentation and classification of white blood SMEAR images using modified CNN architectureDiscover Applied Sciences10.1007/s42452-024-06139-y6:11Online publication date: 2-Nov-2024
  • (2023)Chaotic fitness-dependent quasi-reflected Aquila optimizer for superpixel based white blood cell segmentationNeural Computing and Applications10.1007/s00521-023-08486-035:21(15315-15332)Online publication date: 9-Apr-2023
  • (2022)Experimental comparison of ten state-of-the-art saliency detection algorithms for segmenting leukocyte nucleus2022 Conference on Information Communications Technology and Society (ICTAS)10.1109/ICTAS53252.2022.9744693(1-7)Online publication date: Mar-2022

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