Spatio-temporal modeling of lung images for cancer detection

Shen, Li; Zheng, Wei; Gao, Ling; Huang, Heng; Makedon, Fillia; Pearlman, Justin D.

doi:10.3892/or.15.4.1085

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Based on our previous work, 4 we have developed STAT, a spatio-temporal analysis tool for modeling pulmonary nodules in pMRI image sequences. STAT extends our previous study by creating an easy-to-use interface as well as incorporating many new functions.…”

Section: Discussionmentioning

confidence: 99%

“…We have investigated this problem in a prior study 4 and proposed an automated spatio-temporal modeling framework for pulmonary nodules in pMRI sequences. The resulting spatio-temporal model can lead to many types of nodule characterization, e.g., a time intensity profile of a nodule region; and be used to capture important angiogenic patterns in the lung that can distinguish between cancer and benign nodules and help early detection.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal analysis tool for modeling pulmonary nodules in MR images

et al. 2006

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To detect lung cancer at an earlier stage, a promising method is to apply perfusion magnetic resonance imaging (pMRI) modified to assess tumor angiogenesis. One key issue is to effectively characterize angiogenic patterns of pulmonary nodules. Based on our previous study addressing this issue, in this work, we develop STAT, a Spatio-Temporal Analysis Tool that implements not only our previously proposed pulmonary nodule modeling framework but also a user friendly interface and many extended functions. Our goal is to make STAT an easyto-use tool that can be applied to more general cases. STAT employs the following overall strategy for modeling pulmonary nodules: (1) nodule identification using a correlation maximization method, (2) nodule segmentation using edge detection, morphological operations and model-based strategy, and (3) nodule registration using landmark approach and thin-plate spline interpolation. In nodule identification, STAT provides new schemes for selecting the template and refining results in difficult cases. In nodule segmentation, STAT provides additional flexibilities for creating the weighting mask, selecting morphological structure elements and individually fixing segmentation result. In nodule registration, our previous study uses principal component analysis for landmark extraction, which may not work in general. To overcome this limitation, STAT provides an enhanced approach that minimizes the bending energy of the thin plate spline interpolation or mean square distance between each landmark set and the template set. Our main application of STAT is to define blood arrival patterns in the lung to identify tumor angiogenesis as a means of early accurate diagnosis of cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatio-temporal analysis tool for modeling pulmonary nodules in MR images

et al. 2006

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“…Shen et al utilize perfusion magnetic resonance imaging (pMRI), an important tool in assessing tumor angiogenesis for the early detection of lung cancer (20). Their study demonstrates a novel integrated framework for spatio-temporal modeling of pulmonary nodules in pMRI image sequences.…”

Section: Intelligent Accessing Retrieving and Storing Of Medical Imagesmentioning

confidence: 99%

Overview: Computational analysis and decision support systems in oncology

Makedon

Karkaletsis

2006

Oncol Rep

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Computational analysis tools and decision support systems have increased their penetration in the support of clinical processes and management of medical data and knowledge. Applications range from adjunct tools for diagnosis and disease investigation to the treatment and monitoring of therapeutic procedures. As all medical fields, the field of oncology is affected. This special issue includes studies presenting research and applications of computational intelligence in oncology, covering four main areas: i) decision support systems (DSS) and artificial intelligence (AI) applications in oncology; ii) design and assessment of classification tools in oncology; iii) intelligent accessing, retrieving, and storing of medical images; and iv) intelligent telemedicine and telehealth applications in oncology.

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Automatic evaluation of the progress of bacterial pulmonary infections in temporal radiographic image sequences

Tsevas

Iakovidis

2009

2009 9th International Conference on Information Technology and Applications in Biomedicine

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Spatio-temporal modeling of lung images for cancer detection

Cited by 3 publications

References 3 publications

Spatio-temporal analysis tool for modeling pulmonary nodules in MR images

Spatio-temporal analysis tool for modeling pulmonary nodules in MR images

Overview: Computational analysis and decision support systems in oncology

Automatic evaluation of the progress of bacterial pulmonary infections in temporal radiographic image sequences

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