Enhancement of low-resolution HEp-2 cell image classification using partial least-square regression

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-resoluti...

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Bibliographic Details
Main Authors: Anam, Asaad M., Rushdi, Muhammad A., Fahmy, Ahmed S.
Format: Conference Proceeding
Language:English
Subjects:
Computer-aided diagnosis (CAD)
Correlation
Feature extraction
feature learning
HEp-2 cell classification
Image resolution
Microscopy
partial least-square regression
super-resolution
Support vector machines
Testing
Training
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Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP.2016.7532557