Texture-Based Leukocyte Image Retrieval Using Color Normalization And Quaternion Fourier Transform Based Segmentation

Authors

  • Prabir Sarkar School of Medical Science and Technology
  • Madhumala Ghosh School of Medical Science and Technology
  • Chandan Chakraborty School of Medical Science and Technology

Keywords:

CBIR, Quaternion Fourier transform, Gabor wavelet, CML Leukocytes, blood smear image

Abstract

This work aims to develop texture-based microscopic image retrieval methodology for leukocyte recognition. This approach includes four consecutive steps viz. color normalization technique, noise reduction, segmentation method and textural feature extraction. The quadratic approach provides best normalization which is followed by noise removal by median filter. Comparative studies among five
filters have been experimented to search the best suited filter for this particular type of microscopic image of peripheral blood smears. In segmentation step, the segmentation is done by quaternion Fourier transform (QFT) and this method is compared with literature. The comparative study reveals that amongst three segmentation techniques, quaternion Fourier transform gives higher segmentation accuracy (92.13%) to identify the leukocyte nucleus. Comparative analysis in feature extraction step shows that Gabor-wavelet features provides higher precision i.e., 82.5% for (normal leukocyte) query image and histogram based features gives the better precision
(76.53%) for recognizing abnormal leukocyte.

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Published

2013-01-01

How to Cite

Prabir Sarkar, Madhumala Ghosh, & Chandan Chakraborty. (2013). Texture-Based Leukocyte Image Retrieval Using Color Normalization And Quaternion Fourier Transform Based Segmentation. Journal of Network and Innovative Computing, 1, 13. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/18

Issue

Vol. 1 (2013)

Section

Original Article

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