Texture Classification of Diffused Liver Diseases Using Wavelet Transforms

Document Type: Original Paper

Authors

1 M.Sc. in Medical Physics, Tehran University of Medical Sciences, Tehran, Iran.

2 Assistant Professor, Physics and Biomedical Engineering Dept. Tehran University of Medical Sciences, Tehran, Iran.

3 Assistant Professor, Physics and Biomedical Engineering Dept. Tehran University of Medical Sciences, Tehran, Iran. Research Center for Science & Technology in Medicine, Imam Khomeini Hospital, Tehran, Iran.

4 Assistant Professor, Radiology Dept., Tehran University of Medical Sciences, Tehran, Iran.

Abstract

Introduction: A major problem facing the patients with chronic liver diseases is the diagnostic procedure. 
The conventional diagnostic method depends mainly on needle biopsy which is an invasive method. There 
are  some  approaches  to  develop  a  reliable  noninvasive  method  of  evaluating  histological  changes  in 
sonograms. The main characteristic used to distinguish between the normal, hepatitis and cirrhosis liver is the 
texture of liver surface. The problem of defining a set of meaningful features that explores the characteristics 
of the texture, leads to several methods of determining tissue texture. Some of these methods, which have 
been developed so far, are based on wavelet transform. The selection of wavelet transform type affects the 
accuracy of determining the texture. In this study, an optimal wavelet transform called Gabor wavelet was 
introduced and three different methods of determining tissue texture were evaluated. These include statistical, 
dyadic wavelet transform and Gabor wavelet transform methods.   
Materials and Methods: The proposed algorithm was applied to differentiate ultrasonic liver images into 
two disease states (hepatitis and cirrhosis) and normal liver. In this experiment, 50 liver sample images for 
each three states which already been proven by needle biopsy were used. These images are taken from a 
Toshiba Sonolayer SSA250A device using a 3.75 MHz transducer. For each image, a region of interest (ROI) 
with 75×35 pixels is selected. The ROI is chosen to include only liver tissue without major blood vessel or 
hepatic duct. 
The  classification  method  used  for  this  work  is  "Minimum  Distance",  where  the  distance  is  calculated 
between feature vectors of test image and reference images. In order to evaluate the diagnostic results, two 
quantities named “Sensitivity” and “Specificity” were calculated for each method. 
Results: The obtained results show that Gabor wavelet has 85% and dyadic wavelet has 77% sensitivity in 
the  hepatitis  liver  images.  On  the  other  hand,  Gabor  wavelet  shows  86%  sensitivity  in the cirrhosis liver 
images, while dyadic wavelet has 78%. The specificity of Gabor wavelet in the hepatitis and cirrhosis liver 
images is 77% and 79% respectively, while the specificity of dyadic wavelet is 65% and 72%, respectively. 
Discussion  and  Conclusion:    Based  on  this  experiment,  the  Gabor  wavelet  is  more  appropriate  than  the 
dyadic wavelet and statistical based method for the texture classification as it leads to higher classification 
accuracy,  because  the  dyadic  wavelet  loses  some  middle-band  information,  while  the  Gabor  wavelet 
preserves it. Based on what was observed, the most significant information regarding the texture is mainly 
located in the middle-frequency bands of wavelet decomposition. Therefore, using Gabor wavelet, a more 
flexible decomposition of the entire frequency band can be achieved leading to a superior differentiation of 
the texture information.

Keywords

Main Subjects