Prostate Tissue Characterization Using TRUS Image Spectral Features

  • S. S. Mohamed
  • A. M. Youssef
  • E. F El-Saadany
  • M. M. A. Salama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


In this paper focuses on extracting and analyzing spectral features from Trans-Rectal Ultra-Sound (TRUS) images for prostate tissue characterization. The information of the images’ frequency domain features and spatial domain features are used to achieve an accurate Region of Interest (ROI) identification. In particular, each image is divided into ROIs by the use of Gabor filters, a crucial stage, where the image is segmented according to the frequency response of the image pixels. Further, pixels with a similar response to the same filter are assigned to the same region to form a ROI. The radiologist’s experience is also integrated into the algorithm to identify the highly suspected ROIs.

Next, for each ROI, different spectral feature sets are constructed. One set includes the power spectrum wedge and ring energies. The other sets are constructed using geometrical features extracted from the Power Spectrum Density (PSD). In particular, the estimated PSD in these sets is divided into two segments. Polynomial interpolation is used for the first segment and the obtained polynomial coefficients are used as features. The second segment is approximated by a straight line and the slope, the Y intercept as well as the first maximum reached by the PSD are considered as features.

A classifier-based feature selection algorithm using CLONALG, a recently proposed optimization technique developed on the basis of clonal selection of the Artificial Immune System (AIS), is adopted and used to select an optimal subset from the above extracted features. Using different PSD estimation techniques, the obtained accuracy ranges from 72.2% to 93.75% using a Support Vector Machine classifier.


Feature Subset Prostate Volume Power Spectrum Density Artificial Immune System Feature Selection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. S. Mohamed
    • 1
  • A. M. Youssef
    • 2
  • E. F El-Saadany
    • 1
  • M. M. A. Salama
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of WaterlooCanada
  2. 2.Concordia Institute for Information Systems EngineeringConcordia UniversityMontréalCanada

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