ArticleJournal of medical systems2017
Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric.
Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network.Journal of imaging informatics in medicine · 2024Article
- Article
- Cold-hot nature identification of Chinese herbal medicines based on the similarity of HPLC fingerprints.Frontiers in chemistry · 2022Article
- Application of Recurrent Neural Network Algorithm in Intelligent Detection of Clinical Ultrasound Images of Human Lungs.Computational intelligence and neuroscience · 2022Article
- A Biomorphic Model of Cortical Column for Content-Based Image Retrieval.Entropy (Basel, Switzerland) · 2021Article
- The Effects of Perinodular Features on Solid Lung Nodule Classification.Journal of digital imaging · 2021Article
- Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.Medical image analysis · 2021Article
- Lung cancer histology classification from CT images based on radiomics and deep learning models.Medical & biological engineering & computing · 2021Article
- Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification.Journal of digital imaging · 2020Article
- Article
- An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images.Sensors (Basel, Switzerland) · 2019Review
- Overview on subjective similarity of images for content-based medical image retrieval.Radiological physics and technology · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Similarity measurement of lung nodules is a critical component in content-based image retrieval (CBIR), which can be useful in differentiating between benign and malignant lung nodules on computer tomography (CT). This paper proposes a new two-step CBIR scheme (TSCBIR) for computer-aided diagnosis of lung nodules. Two similarity metrics, semantic relevance and visual similarity, are introduced to measure the similarity of different nodules. The first step is to search for K most similar reference ROIs for each queried ROI with the semantic relevance metric. The second step is to weight each retrieved ROI based on its visual similarity to the queried ROI. The probability is computed to predict the likelihood of the queried ROI depicting a malignant lesion. In order to verify the feasibility of the proposed algorithm, a lung nodule dataset including 366 nodule regions of interest (ROIs) is assembled from LIDC-IDRI lung images on CT scans. Three groups of texture features are implemented to represent a nodule ROI. Our experimental results on the assembled lung nodule dataset show good performance improvement over existing popular classifiers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.