Simultaneous Sparsity Model for Histopathological Image Representation and Classification

The multi-channel nature of digital histopathological images presents an opportunity to exploit the correlated color channel information for better image modeling. Inspired by recent work in sparsity for single channel image classification, we propose a new simultaneous sparsity model for multi-chan...

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Vydáno v:IEEE transactions on medical imaging Ročník 33; číslo 5; s. 1163 - 1179
Hlavní autoři: Srinivas, Umamahesh, Mousavi, Hojjat Seyed, Monga, Vishal, Hattel, Arthur, Jayarao, Bhushan
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Animals
Biomedical image processing
Biomedical imaging
Breast - pathology
Breast Neoplasms - pathology
Classification
Databases, Factual
Female
Histocytochemistry - methods
histopathological image analysis
Humans
Image analysis
Image color analysis
Image Processing, Computer-Assisted - methods
Image reconstruction
Kidney - pathology
multichannel images
ROC Curve
Sparse representation
Sparsity
Spleen - pathology
ISSN:0278-0062, 1558-254X, 1558-254X
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Shrnutí:The multi-channel nature of digital histopathological images presents an opportunity to exploit the correlated color channel information for better image modeling. Inspired by recent work in sparsity for single channel image classification, we propose a new simultaneous sparsity model for multi-channel histopathological image representation and classification (SHIRC). Essentially, we represent a histopathological image as a sparse linear combination of training examples under suitable channel-wise constraints. Classification is performed by solving a newly formulated simultaneous sparsity-based optimization problem. A practical challenge is the correspondence of image objects (cellular and nuclear structures) at different spatial locations in the image. We propose a robust locally adaptive variant of SHIRC (LA-SHIRC) to tackle this issue. Experiments on two challenging real-world image data sets: 1) mammalian tissue images acquired by pathologists of the animal diagnostics lab (ADL) at Pennsylvania State University, and 2) human intraductal breast lesions, reveal the merits of our proposal over state-of-the-art alternatives. Further, we demonstrate that LA-SHIRC exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training per class is often not available.
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2014.2306173