Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach
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Summary
This work proposes a novel approach for initializing an active shape model (ASM) and applies it to 3D lung segmentation in CT scans and shows a statistically significant improvement compared to four other approaches.
- Type
- article
- Published
- 2014-10-21
- Cited by
- 39
- References
- 19
- Access
- Open access
- OpenAlex
- https://openalex.org/W25400660
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2677687
Keywords
Arctic, Environmental science, Methane, Oceanography, The arctic
References
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- A Game-Theoretic Framework for Landmark-Based Image Segmentation
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- Reconstructing a 3D structure from serial histological sections
- Active Shape Models: Evaluation of a Multi-Resolution Method for Improving Image Search
- Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge
- Robust model-based detection of the lung field boundaries in portable chest radiographs supported by selective thresholding
- Gradient vector flow based active shape model for lung field segmentation in chest radiographs
- Distinctive Image Features from Scale-Invariant Keypoints
- Active shape model segmentation with optimal features
- SIFT-Rank: Ordinal description for invariant feature correspondence
- Scale & Affine Invariant Interest Point Detectors
- An automated initialization system for robust model-based segmentation of lungs in CT data
- Medical Image Computing and Computer-Assisted Intervention — MICCAI’98
- Lung region segmentation based on multi-resolution Active Shape Model
- A performance evaluation of local descriptors
Cited by
- Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT
- Imaging texture analysis for automated prediction of lung cancer recurrence after stereotactic radiotherapy
- A Feature-based Approach to Big Data Analysis of Medical Images
- Automatic volumetric localization of the liver in abdominal CT scans using low level processing and shape priors
- Überatlas: Fast and robust registration for multi-atlas segmentation
- An Approach for Reducing the Error Rate in Automated Lung Segmentation
- Radiomics for Response Assessment after Stereotactic Radiotherapy for Lung Cancer
- Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation
- A wavelet frames + K-means based automatic method for lung area segmentation in multiple slices of CT scan
- Genetic programming for evolving figure-ground segmentors from multiple features
- Patient-Specific Model Based Segmentation of Lung Computed Tomographic Images
- Optimizing the cervix cytological examination based on deep learning and dynamic shape modeling
- Visual Three-Dimensional Reconstruction of Aortic Dissection Based on Medical CT Images
- A new model-based framework for lung tissue segmentation in three-dimensional thoracic CT images
- Deformable image registration applied to lung SBRT: Usefulness and limitations.
- Figure-ground image segmentation using feature-based multi-objective genetic programming techniques
- A New Hybrid Approach Using Fuzzy Clustering and Morphological Operations for Lung Segmentation in Thoracic CT Images
- Genetic Programming for Supervised Figure-ground Image Segmentation
- Graph-based unsupervised segmentation for lung tumor CT images
- Automatic Aortic Dissection Recognition Based on CT Images
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