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Next: Feature Selection and Reduction Up: Results Previous: Classification Using Zernike Features

Classification Using Haralick Texture Features

Other types of numerical features were also explored for their ability to describe protein localization patterns. A set of descriptive features that are fundamentally different from the Zernike moments, the texture features described by Haralick [28], were investigated next. These features were selected because they can be made invariant to translations and rotations, and because they describe more intuitive aspects of the images (e.g. coarse versus smooth, directionality of the pattern, image complexity, etc.) using statistics of the gray-level co-occurrence matrix for each image.

Since the BPNN classifier proved more effective than the classification tree when using the Zernike features, it was used with the texture features. This time the network had only 13 inputs but still had 20 hidden nodes and 5 output nodes, all fully connected. Training was carried out as before and the results are shown in Table 2.6. The average performance of this feature set/classifier combination, $88\pm5.1\%$ - corresponding to a predicted accuracy of 99.6% for majority rule on 10 images - is very close to that of the Zernike moment/BPNN approach. Note that this performance is accomplished with far fewer features describing each image, 13 texture features versus 49 Zernike moments.


  
Table 2.6: Confusion matrix generated from the output of a back-propagation neural network trained and tested with the Haralick texture features. The average classification rate for the test data is $88\pm5.1\%$ (mean $\pm$ 95% confidence interval). Performance across the 8 test sets ranged from 70-98%. Average performance on the training data was $89\pm4.6\%$.
True Output of the BPNN
Classification Giantin Hoechst LAMP2 NOP4 Tubulin
Giantin 97% 0% 3% 0% 0%
Hoechst 3% 93% 3% 0% 0%
LAMP2 8% 0% 82% 8% 2%
NOP4 0% 0% 13% 88% 0%
Tubulin 4% 0% 8% 8% 81%


next up previous contents
Next: Feature Selection and Reduction Up: Results Previous: Classification Using Zernike Features
Copyright ©1999 Michael V. Boland
1999-09-18