The RGB image was converted to L*u*v color space

Two unsupervised methods were used:

• MRF segmentation ( Kato et al. )
  • EM step
  • ICM
• K-means

Parameters:
User defined: # of regions, â, temperature.

• User defined
• User chooses the desired region for classification
• The first order statistics (mean, variance, skewness, kurtosis, range) are calculated for a ROI around the selected image Automated
• Skeletonization technique was applied for each segmented region
• A sliding ROI (21 x 21) was used to extract first order statistics
• K-nearest neighbor classifier was used (NN)


• Segmented area is also calculated
  

Segmentation Stage:

• Intensity value channel U
• Intensity value channel V

Classification Stage:

• Mean value
• Standard deviation
• Kurtosis
• Skewness
• Range

Segmentation Stage:

• Visual evaluation seams to present good results
• No serious evaluation was conducted
• Segmentation process is painfully slow
• Dataset is too small to construct robust learning process

Segmentation process:

• Evaluation of more complex techniques

Classification process:

• Bigger training database
• Other texture features
• Try different classifier

Evaulation:

• Use of ground truth and shape differensiation metrics