Intraoperative detection of insufficient surgical margins in head and neck cancer resection using dual aperture fluorescence ratio imaging

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Evaluation of the channelized Hotelling observer with an internal-noise model in a train-test paradigm for cardiac SPECT defect detection

En este objeto de información se dará una aproximación a las demencias su definición y dar a conocer que demencia no es igual a envejecimiento.1.0Acceso a Interne

CONTENT-ADAPTIVE 3D MESH MODELING FOR REPRESENTATION OF VOLUMETRIC IMAGES

ABSTRACT In this work we present a fast, content-adaptive approach for three-dimensional (3D

ICIP-2001 paper proposal: Tomographic Image Reconstruction Using Content-Adaptive Mesh Modeling

In this work we propose the use of a content-adaptive mesh model (CAMM) for tomographic image reconstruction. In the proposed framework, the image to be reconstructed is first modeled by an efficient mesh representation. The image is then obtained through estimation of the nodal values from the measured data. The use of a CAMM can greatly alleviate the ill-posed nature of the reconstruction problem, thereby leading to improved quality in the reconstructed images. In addition, it can also lead to development of efficient numerical reconstruction algorithms. The proposed methods are tested using gated cardiac-perfusion images. Results demonstrate that the proposed approach achieves the best performance when compared to several commonly used methods for image reconstruction, and produces results very rapidly. 1

Image Sequence Segmentation Based on a Similarity Metric 1

Abstract--I

MULTI-MODALITY TOMOGRAPHIC IMAGE RECONSTRUCTION USING MESH MODELING 1

In this paper we investigate a mesh-modeling approach for multi-modality image reconstruction. In the proposed approach a mesh model uses information obtained from an anatomical MR image to aid in reconstruction of PET images. The aim is to improve spatial resolution and quantitative accuracy of the PET image by using anatomical boundary information from the MR image. The mesh approach accomplishes this by using spatially adaptive spatial sampling and smoothing in the PET reconstruction. Our preliminary results demonstrate that this mesh-based approach to multi-modality PET reconstruction can achieve good results at low computational cost. 1