
Proceedings Paper
Automatic classification and detection of clinically relevant images for diabetic retinopathyFormat | Member Price | Non-Member Price |
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$17.00 | $21.00 |
Paper Abstract
We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of
clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of
the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that
are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR
images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as
bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is
characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns
a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation-
Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a
point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class
bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the
query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag
feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant
images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also
improves the efficiency and accuracy of DR lesion diagnosis and assessment.
Paper Details
Date Published: 17 March 2008
PDF: 9 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69150Q (17 March 2008); doi: 10.1117/12.769858
Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)
PDF: 9 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69150Q (17 March 2008); doi: 10.1117/12.769858
Show Author Affiliations
Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)
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