Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images

Tulasi Gayatri Devi, Nagamma Patil, Sharada Rai, Cheryl Sarah Philipose

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Visual inspection of peripheral blood samples is a critical step in the leukemia diagnostic process. Automated solutions based on artificial vision approaches can accelerate this procedure, while also improving accuracy and uniformity of response in telemedicine applications. In this study, we propose a novel GBHSV-Leuk method to segment and classify Acute Lymphoblastic Leukemia (ALL) cancer cells. GBHSV-Leuk is a two staged process. The first stage involves pre-processing, which uses the Gaussian Blurring (GB) technique to blur the noise and reflections in the image. The second stage involves segmentation using the Hue Saturation Value (HSV) technique and morphological operations to differentiate between the foreground and background colors, which improve the accuracy of prediction. The proposed method attains 96.30% accuracy when applied on the private dataset, and 95.41% accuracy when applied on the ALL-IDB1 public dataset. This work would facilitate early detection of ALL cancer.

Original languageEnglish
Article number348
JournalLife
Volume13
Issue number2
DOIs
Publication statusPublished - 02-2023

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Biochemistry, Genetics and Molecular Biology(all)
  • Space and Planetary Science
  • Palaeontology

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