Automatic CAD System for HEp-2 Cell Image Classification

It has been estimated that autoimmune diseases are among the top ten leading causes of death among women in all age groups up to 65 years. However, the detection of it by indirect immunofluorescence (IIF) image analysis depends heavily on the experience of the physicians. An accurate and automatic Computer Aided Diagnosis (CAD) system will help greatly for the classification of the Human Epithelial type 2 (HEp-2) cell images with little human intervention. In this paper we present an automatic HEp-2 cell image classification technique that exploits different spatial scaled image representation and sparse coding of SIFT features. Additionally, spatial max pooling of sparse coding at different scales is used to boost the classification performance. The proposed method is tested on the ICPR 2012 contest dataset and experiments show that it clearly outperforms state-of-the-art techniques in cell and image level as well as two intensity level images.

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