CT-PET Landmark-based Lung Registration Using a Dynamic Breathing Model

Chambon,; Moreno,; Santhanam,; Rolland,; Angelini, Elsa D.; Bloch,

doi:10.1109/iciap.2007.4362857

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…Further applications include the use of the segmentation of the heart in registration algorithms based on structures [43] or based on features/landmarks [44], necessary even in PET/CT and SPECT/CT combined devices, and subsequently, in radiotherapy planning procedures.…”

Section: Resultsmentioning

confidence: 99%

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Moreno

Takemura

Colliot

et al. 2008

Pattern Recognition

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International audienceSegmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach

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Section: Resultsmentioning

confidence: 99%

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Moreno

Takemura

Colliot

et al. 2008

Pattern Recognition

Self Cite

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“…Establishing spatial correspondence between volume images is an important and fundamental computational problem in 3D image and video processing. It can greatly benefit many tasks such as nonrigid image registration [1][2][3][4], registration evaluation [5][6][7][8], volumetric ultrasound panorama [9], object recognition [10], and three-dimensional classification [11], to name a few. In biomedical computation, many medical scans (CT and MRI) are volumetric images, whose spatial and temporal correspondence are critical in clinical monitoring, diagnosis, and treatment planning [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Effective Volumetric Feature Modeling and Coarse Correspondence via Improved 3DSIFT and Spectral Matching

Chen

2014

Mathematical Problems in Engineering

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This paper presents a nonrigid coarse correspondence computation algorithm for volumetric images. Our matching algorithm first extracts then correlates image features based on a revised and improved 3DSIFT (I3DSIFT) algorithm. With a scale-related keypoint reorientation and descriptor construction, this feature correlation is less sensitive to image rotation and scaling. Then, we present an improved spectral matching (ISM) algorithm on correlated features to obtain a one-to-one mapping between corresponded features. One can effectively extend this feature correspondence to dense correspondence between volume images. Our algorithm can benefit nonrigid volumetric image registration in many tasks such as motion modeling in medical image analysis and processing.

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“…Chambon (Chambon et al, 2007) presented a CT-PET landmark-based registration method that uses a breathing model to guarantee physiologically plausible deformations.…”

Section: Introductionmentioning

confidence: 99%

Intra-Patient Registration Methods for Thoracic Ct Exams

Silva¹,

Cancela²,

Teixeira³

2009

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Now-a-days CT scanners provide detailed morphological information of pulmonary structures, with great importance to the diagnostic and follow-up of oncological diseases. When a patient with lung cancer is submitted to several CT exams during a period of time; these exams need an appropriate registration to quantify or visualize the tumour's evolution. We propose a new method for 3D intra-patient registration of thoracic CT exams and compare its results with several 3D registration methods. The performance of these registration methods is analysed, computing several normalized figures of merit; we also explore these metrics to check which is more sensible to changes in CT exams due to the presence of lung tumours. The results with several cases of intra-patient, intra-modality registration show that the proposed method provides an accurate registration which is needed for the quantitative tracking of lesions that may effectively assist the follow-up process of oncological patients.

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CT-PET Landmark-based Lung Registration Using a Dynamic Breathing Model

Cited by 8 publications

References 26 publications

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Effective Volumetric Feature Modeling and Coarse Correspondence via Improved 3DSIFT and Spectral Matching

Intra-Patient Registration Methods for Thoracic Ct Exams

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