ArticleInternational journal of computer assisted radiology and surgery2016
Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images.
Article in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Pretreatment Tumor Volume and Tumor Sphericity as Prognostic Factors in Patients with Oral Cavity Squamous Cell Carcinoma: A Prospective Clinical Study in 95 Patients.Journal of personalized medicine · 2023Article
- CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening.Computer methods and programs in biomedicine · 2021Article
- CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.International journal of computer assisted radiology and surgery · 2020Article
- Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems.BioMed research international · 2020Article
- Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval.Journal of digital imaging · 2019Article
- Identification of Nonaggressive Pulmonary Nodules Using an Optimized Scoring System.Journal of thoracic imaging · 2019Observational
- Semi-quantitative analysis of pre-treatment morphological and intratumoral characteristics usingQuantitative imaging in medicine and surgery · 2018Article
- Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules.Biomedical engineering online · 2018Review
- Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.Journal of digital imaging · 2017Article
- Article
- A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.Journal of digital imaging · 2016Article
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5 authors.
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Abstract
purposeBoundary roughness of a pulmonary nodule is an important indication of its malignancy. The irregularity of the shape of a nodule is represented in terms of a few diagnostic characteristics such as spiculation, lobulation, and sphericity. Quantitative characterization of these diagnostic characteristics is essential for designing a content-based image retrieval system and computer-aided system for diagnosis of lung cancer.
methodsThis paper presents differential geometry-based techniques for computation of spiculation, lobulation, and sphericity using the binary mask of the segmented nodule. These shape features are computed in 3D considering complete nodule.
resultsThe performance of the proposed and competing methods is evaluated in terms of the precision, mean similarity, and normalized discounted cumulative gain on 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The proposed methods are comparable to or better than gold standard technique. The reproducibility of proposed feature extraction techniques is evaluated using RIDER coffee break data set. The mean and standard deviation of the percent change of spiculation, lobulation, and sphericity are [Formula: see text], [Formula: see text], and [Formula: see text] %, respectively.
conclusionThe prior works of computation of spiculation, lobulation, and sphericity require a set of four ground truths from radiologists and, hence, can not be used in practice. The proposed methods do not require ground truth information of nodules from radiologists, and hence, it can be used in real-life computer-aided diagnosis system for lung cancer.
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