Automated classification of HEp-2 cell images is crucial for fast and accurate detection of autoimmune diseases. Recent competitions resulted in high classification rates on publicly available datasets. However, performance on low-resolution HEp-2 images typically lagged behind that of high-resolution images due to the blurring and sub-sampling of fine cellular details. Direct interpolation of low-resolution images couldn't fill in the performance gap. We propose a learning-based approach to infer high-resolution features from low-resolution HEp-2 images. Our approach exploits partial least-square (PLS) to linearly project low- and high-resolution HEp-2 image features into a common linear subspace where the features are highly correlated. Experimental and statistical evaluations show significant improvement in classification performance due to our technique in comparison with direct interpolation and canonical correlation analysis techniques.
['Asaad M. Anam', 'Muhammad Rushdi', 'Ahmed S. Fahmy']
Enhancement of low-resolution HEp-2 cell image classification using partial least-square regression