Feature-Based Image Patch Approximation for Lung Tissue Classification
Abstract
In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.
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Both [38] and [39] use leave-one-patient-out (LOO), which is arguably much easier and not directly comparable to 10-fold CV [54] or our Patch-CV5.
“Both [38] and [39] use the evaluation protocol of “leave-one-patient-out” (LOO), which is arguably much easier and not directly comparable to 10-fold CV [54] or our Patch-CV5.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [38]
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- Authors
- Song, Y., Cai, W., Zhou, Y., & Feng, D. D.
- Affiliations
- The University of Sydney (AU)
- Johns Hopkins University (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2013-04
- Volume
- 32
- Issue
- 4
- Pages
- 797-808
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W1975020933
- Cited by
- 177
- References
- 38
- Citations by year
- 2026: 1 · 2025: 13 · 2024: 5 · 2023: 14 · 2022: 11 · 2021: 11 · 2020: 13 · 2019: 16 · 2018: 21 · 2017: 22 · 2016: 17 · 2015: 15 · 2014: 14 · 2013: 4
- FWCI
- 12.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- lung tissue classification, high-resolution computed tomography, sparse approximation, texture features, interstitial lung disease
- MeSH terms
- Algorithms, Humans, Image Processing, Computer-Assisted, Lung, Models, Biological, Tomography, X-Ray Computed, Databases, Factual, Lung Diseases, Interstitial
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