ArticleBiomedical engineering letters2019
Segmentation of lung fields from chest radiographs-a radiomic feature-based approach.
Article in Biomedical engineering letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- Heatmap-Based Active Shape Model for Landmark Detection in Lumbar X-ray Images.Journal of imaging informatics in medicine · 2025Article
- A Method for Real-Time Lung Nodule Instance Segmentation Using Deep Learning.Life (Basel, Switzerland) · 2024Article
- Study on lung CT image segmentation algorithm based on threshold-gradient combination and improved convex hull method.Scientific reports · 2024Article
- Photoacoustic imaging aided with deep learning: a review.Biomedical engineering letters · 2022Review
- Deep learning-based automatic detection of tuberculosis disease in chest X-ray images.Polish journal of radiology · 2022Article
- 2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation.Journal of digital imaging · 2021Article
- Analysis of Tuberculosis in Chest Radiographs for Computerized Diagnosis using Bag of Keypoint Features.Journal of medical systems · 2019Article
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3 authors.
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Abstract
Precisely segmented lung fields restrict the region-of-interest from which radiological patterns are searched, and is thus an indispensable prerequisite step in any chest radiographic CADx system. Recently, a number of deep learning-based approaches have been proposed to implement this step. However, deep learning has its own limitations and cannot be used in resource-constrained settings. Medical systems generally have limited RAM, computational power, storage, and no GPUs. They are thus not always suited for running deep learning-based models. Shallow learning-based models with appropriately selected features give comparable performance but with modest resources. The present paper thus proposes a shallow learning-based method that makes use of 40 radiomic features to segment lung fields from chest radiographs. A distance regularized level set evolution (DRLSE) method along with other post-processing steps are used to refine its output. The proposed method is trained and tested using publicly available JSRT dataset. The testing results indicate that the performance of the proposed method is comparable to the state-of-the-art deep learning-based lung field segmentation (LFS) methods and better than other LFS methods.
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