ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2018
A level-set approach to joint image segmentation and registration with application to CT lung imaging.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans.Archives of medical science : AMS · 2026Article
- GPU-accelerated lung CT segmentation based on level sets and texture analysis.Scientific reports · 2024Article
- A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images.Diagnostics (Basel, Switzerland) · 2023Review
- Image-Processing-Based Subway Tunnel Crack Detection System.Sensors (Basel, Switzerland) · 2023Article
- Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review.Multimedia tools and applications · 2022Article
- Automatic clustering method to segment COVID-19 CT images.PloS one · 2021Article
- Enhanced super-resolution reconstruction of T1w time-resolved 4DMRI in low-contrast tissue using 2-step hybrid deformable image registration.Journal of applied clinical medical physics · 2020Article
- A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction.Computational and mathematical methods in medicine · 2020Article
- Discontinuity Preserving Liver MR Registration with 3D Active Contour Motion Segmentation.IEEE transactions on bio-medical engineering · 2018Article
- PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma.PloS one · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Automated analysis of structural imaging such as lung Computed Tomography (CT) plays an increasingly important role in medical imaging applications. Despite significant progress in the development of image registration and segmentation methods, lung registration and segmentation remain a challenging task. In this paper, we present a novel image registration and segmentation approach, for which we develop a new mathematical formulation to jointly segment and register three-dimensional lung CT volumes. The new algorithm is based on a level-set formulation, which merges a classic Chan-Vese segmentation with the active dense displacement field estimation. Combining registration with segmentation has two key advantages: it allows to eliminate the problem of initializing surface based segmentation methods, and to incorporate prior knowledge into the registration in a mathematically justified manner, while remaining computationally attractive. We evaluate our framework on a publicly available lung CT data set to demonstrate the properties of the new formulation. The presented results show the improved accuracy for our joint segmentation and registration algorithm when compared to registration and segmentation performed separately.
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Registered trials
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