NDLI: A bag of visual words approach for centromere and cytoplasmic staining pattern classification on HEp-2 images

Content Provider	IEEE Xplore Digital Library
Author	Iannello, G. Onofri, L. Soda, P.
Copyright Year	2012
Description	Author affiliation: Integrated Research Centre, Università Campus Bio-Medico di Roma, Rome, Italy (Iannello, G.; Onofri, L.; Soda, P.)
Abstract	Antinuclear autoantibodies (ANAs) are important markers to diagnose autoimmune diseases, very serious and also invalidating illnesses. The benchmark procedure for ANAs diagnosis is the indirect immunofluorescence (IIF) assay performed on the HEp-2 substrate. Medical doctors first determine the fluorescence intensity exhibited by HEp-2 cells, and then report the staining pattern for positive wells only. With reference to staining pattern recognition, in the literature we found works recognizing five main patterns characterized by well-defined cell edges. These approaches are based on cell segmentation, a task that should be harder than the classification itself. We present here a method extending the panel of detectable HEp-2 staining patterns, introducing the centromere and cytoplasmic patterns, which do not show well-defined cell edges, and where a segmentation-based classification may fail. We apply a local approach which extracts SIFT descriptors and then classifies an image through the bag of visual words approach. This permits to represent complex image contents without applying the segmentation procedure. We test our approach on a dataset of HEp-2 images with large variability in both fluorescence intensity and staining patterns. Despite the large skew of the a-priori class distribution, our system correctly recognizes the 98.3% of samples, with a F-measure equal to 92.3%, 95.2% and 99.0%, for each class.
Starting Page	1
Ending Page	6
File Size	461853
Page Count	6
File Format	PDF
ISBN	9781467320498
ISSN	10637125
e-ISBN	9781467320511
DOI	10.1109/CBMS.2012.6266360
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-20
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Vocabulary Pattern recognition Image segmentation Training Accuracy Image edge detection
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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