Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 35, Issue 3, Pages 738-751Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2015.2493530
Keywords
Histopathological image classification; sparse coding; dictionary learning; feature extraction; cancer grading
Categories
Funding
- Army Research Office (ARO) [W911NF-14-1-0421]
- National Institute of Health NIH grant [KNS070928]
- NCI Cancer Center SupportGrant [NCI P30 CA016672]
- Career Development Award from Brain Tumor SPORE
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1454218] Funding Source: National Science Foundation
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In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.
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