Fuzzy Image Processing for Diagnosing In ammation in Pulmonary Biopsies


1 Department of Industrial Engineering,University of Tehran

2 Department of Industrial Engineering,Amirkabir University of Technology


This paper proposes a new approach to diagnose the degree of in
ammation in digital
images of pulmonary biopsies, provided by a digital camera through a microscope. Diagnosing is done by
detecting thick epithelium cell layers around the vessels and bronchus in tissue images. For analyzing the
complex images of tissue, a fuzzy image processing procedure consisting of ve main stages is presented.
The rst stage is decreasing the complexity of the images by using image pre-processing methods for
enhancement and smoothing the image with a Gaussian low pass lter in order to highlight important
details and ignore the unnecessary parts of the image. The second stage is segmentation by using a fuzzy
c-means clustering algorithm and fuzzy canny edge detection. This step works as a data reduction method
as well as object recognition. Feature extraction, the third stage, will be done by using a fuzzy Hough
transform. After extracting features such as bronchioles and vessels from the image, the fourth stage will
be analysis and reasoning by a fuzzy inference system, which is a hybrid of the Mamdani and Logical
modeling system with a Yager parametric operator. The last stage is tuning system parameters and the
learning process with a feed forward neural network. The output of the proposed algorithm is the degree
of in
ammation inferred by the fuzzy inference system. The proposed approach is user friendly with low
computational time and the results are more precise, reliable and acceptable to experts and physicians.