Iterative Kernel Principal Component Analysis for Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based Image Retrieval Using Gabor-Zernike Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Expert Systems with Applications: An International Journal
Improved kernel principal component analysis for fault detection
Expert Systems with Applications: An International Journal
Using SOM and PCA for analysing and interpreting data from a P-removal SBR
Engineering Applications of Artificial Intelligence
Moment-Based Techniques for Image Retrieval
DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
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In this work, classification of Tuberculosis (TB) digital images has been attempted using active contour method and Differential Evolution based Extreme Learning Machines (DE-ELM). The sputum smear positive and negative images (N=100) recorded under standard image acquisition protocol are subjected to segmentation using level set formulation of active contour method. Moment features are extracted from the segmented images using Hu's and Zernike method. Further, the most significant moment features derived using Principal Component Analysis and Kernel Principal Component Analysis (KPCA) are subjected to classification using DE-ELM. Results show that the segmentation method identifies the bacilli retaining their shape in-spite of artifacts present in the images. It is also observed that with the KPCA derived significant features, DE-ELM performed with higher accuracy and faster learning speed in classifying the images.