Latest results
Improving the performance of the Prony method using a wavelet domain filter for MRI denoising
Rodney Jaramillo, Marianela Lentini, Marco Paluszny
Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine, Volume 2014, Article ID 810680
The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of 𝑇2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is amodification of Kazubek’s algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of 𝑇2 MR images, and the filter is applied to each image before using the variant of the Prony method.
Tissue classification in oncological PET/CT images
Jhonalbert Aponte, David Grande, Wuilian Torres, Miguel Martín-Landrove
Ingeniería y Ciencias Aplicadas: Modelos Matemáticos y Computacionales, E. Dávila, J. Del Río, M. Cerrolaza, R. Chacón (Eds.) SVMNI 2014
Oncological PET/CT is a powerful combination of molecular and structural imaging that allows for early full body cancer disease detection and further treatment and disease evolution monitoring. Recently, oncological PET/CT has been proposed in the assessment of image tumor contouring for treatment planning what implies the need for a confident method for image integration or fusion. In the present work, tumor PET/CT images are analyzed by a segmentation method, k-means clustering, combining the information coming from PET images, through the Standardized Uptake Value (SUV), and CT image information, through the CT number or linear attenuation coefficient, allowing for tissue classification and image segmentation. Results are used for Gross Target Volume (GTV) assessment as a guide in medical practice for SUV level selection in image tumor contouring in targeted treatment applications, such as radiation therapy. Also, SUV distributions for different tumor lesions are obtained and used to assess reference values in diagnostic PET/CT.
Geometry of tumor growth in brain
Miguel Martín-Landrove, Francisco Torres-Hoyos
Ingeniería y Ciencias Aplicadas: Modelos Matemáticos y Computacionales, E. Dávila, J. Del Río, M. Cerrolaza, R. Chacón (Eds.) SVMNI 2014
Tumor growth can be characterized by using scaling analysis methods performed upon the tumor interface; the procedure yields key parameters that define growth geometry according different universality classes. In the present work, results obtained by scaling analysis are shown for tumor lesions in brain, of primary origin, either malignant or benign and metastases. To evaluate different proposed models for tumor growth in brain, several growth simulations for primary brain tumors or gliomas were performed assuming a simple growth model described by a reaction-diffusion differential equation or in this context, a proliferation-invasion equation. The proliferation term was of the logistic type to take into account the limitation of nutrients and oxygen resources on tumor cells. To take into account the differences between grey and white matter for the diffusion parameter, the simulations used the brain tissue database provided by BrainWeb. Simulations were performed for different relations between the diffusion parameters (invasion) and the reaction parameters (proliferation) covering growth conditions from low grade gliomas up to high grade gliomas (glioblastoma multiforme). Scaling analysis results reveal a close correspondence to results previously obtained on tumor magnetic resonance images, which suggests that the simple model used for the computer simulations describes in an appropriate manner tumor growth of gliomas in brain and potentially its use can be extended to describe brain metastases.
