Counting eosinophils in bronchoalveolar lavage fluid images with fuzzy methodology

  • Authors:

  • Mohammad H. Fazel Zarandi;Shaghayegh Norouzzadeh;Shahram Teimourian;Mostafa Moeen

  • Affiliations:

  • Departmant of Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnique), Tehran 15875-4413, Iran;Departmant of Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnique), Tehran 15875-4413, Iran;Immunology, Asthma and Allergy Research Institute, Tehran University of Medical Sciences, Tehran, Iran;Immunology, Asthma and Allergy Research Institute, Tehran University of Medical Sciences, Tehran, Iran

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2009

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Abstract

This paper proposes a new fuzzy approach to count eosinophils, as a measure of inflammation, in bronchoalveolar lavage fluid images, provided by digital camera through microscope. We use fuzzy cluster analysis and fuzzy classification algorithm to determine the number of objects in an image. For this purpose, a fuzzy image processing procedure consisting of five main stages is presented. The first stage is pre-highlighting the objects in the images by using an image pre-processing method for enhancement, which is sharpening the image with the Laplaian high pass filter in order to have acceptable contrast in the image. The second stage is segmentation by clustering with fuzzy c-mean algorithm for portioning. In this stage the clustered data are the rough symbols of objects in the image containing noise. In the third step, first, a Gaussian low pass filter is used for noise reduction. Then, a contrast adoption in the image is done by modifying the membership functions in the image [H.R. Tizhoosh, G. Krell, B. Michaelis, Knowledge-based enhancement of megavoltage images in radiation therapy using a hybrid neuro-fuzzy system, Image and Vision Computing 19(July) (2000) 217-233]. Object recognition, the fourth stage, will be done by using fuzzy labeling for the objects in the image, using a fuzzy classification method. The number of labeled images shows the number of eosinophils in an image which is an index for diagnosing inflammation. The last stage is tuning parameters and verification of the system performance by using a feed forward Neural Network.