Showing papers in "Computerized Medical Imaging and Graphics in 2008"

TL;DR: This paper investigates and proposes a set of optimally adjusted morphological operators to be used for exudate detection on diabetic retinopathy patients' non-dilated pupil and low-contrast images and results are successful.

TL;DR: An Internet-based melanoma screening system that separates the tumor area from the surrounding skin using highly accurate dermatologist-like tumor area extraction algorithm, and classifies the tumor as melanoma or nevus using a neural network classifier, and presents the diagnosis.

TL;DR: A modified FCM algorithm (called mFCM later) for MRI brain image segmentation is presented, realized by incorporating the spatial neighborhood information into the standardFCM algorithm and modifying the membership weighting of each cluster.

TL;DR: A lung segmentation algorithm called adaptive border marching (ABM), whose novelty lies in the fact that it smoothes the lung border in a geometric way and can be used to reliably include juxtapleural nodules while minimizing oversegmentation of adjacent regions such as the abdomen and mediastinum is presented.

TL;DR: A machine learning approach to the detection of blue-white veil and related structures in dermoscopy images is presented, which involves contextual pixel classification using a decision tree classifier.

TL;DR: An enhancement algorithm that improves image contrast based on local statistical measures of the mammograms is proposed, and a region-ranking system is presented that identifies the regions most likely to represent abnormalities based on the features computed.

TL;DR: An approach that enables synergistic fusion between the 3D CT data and the bronchoscopic video is described and the integrated planning and guidance system and the internal CT-video registration and fusion methods are described.

TL;DR: A hierarchical medical image classification method including two levels using a perfect set of various shape and texture features, including a tessellation-based spectral feature as well as a directional histogram has been proposed.

TL;DR: A content-based image retrieval framework for diverse collections of medical images of different modalities, anatomical regions, acquisition views, and biological systems and an adaptive similarity fusion approach based on a linear combination of individual feature level similarities are presented.

TL;DR: The proposed method has potential application in medical image segmentation, including diagnosis of diseases, and Statistically, the improvement in segmentation was significant for most of the organs considered herein.

TL;DR: A novel joint histogram estimation method (HPV) is presented by using an approximate function of Hanning windowed sinc as kernel function of partial volume interpolation and gives a new method estimating the gradient of mutual information with respect to the model parameters during non-rigid registration.

TL;DR: The fractal dimension (FD) was used to reveal brain structure irregularities in patients with schizophrenia, and showed that the patients had larger FD values than the controls, for the whole brain volume and right hemisphere.

TL;DR: A deconvolution method originating from magnetic resonance techniques and applied to the calculation of dynamic contrast enhanced computed tomography perfusion imaging renders the analysis independent of tracer arrival time to improve the results.

TL;DR: A new active contour model (ACM), called Poisson Gradient Vector Flow (PGVF), with genetic algorithm (GA) constructs a scheme to automatically find the contour of liver in the PET images.

TL;DR: PSO method made image segmentation and feature extraction more valid and accurate, and the ANN models were sophisticated in processing image information.

TL;DR: A technique using stochastic resonance (SR)-based wavelet transform for the enhancement of unclear diagnostic ultrasound images that enhances the edges more clearly and can also optimally enhance an image even if the image noise level is considerable.

TL;DR: The method presented can be considered a generalist approach for the segmentation of the glottal space because, in contrast with other methods found in literature, this approach does not need either initialization or finding strict environmental conditions extracted from the images to be processed.

TL;DR: This work presents a novel interactive method based on a 3D Livewire approach for segmenting complex objects of arbitrary topologies that automatically and seamlessly handles objects with branchings, concavities, protrusions, and non-spherical topologies with minimal user-input.

TL;DR: The proposed method for automatic detection of different stages of MS lesions in the brain magnetic resonance (MR) images, in fluid attenuated inversion recovery (FLAIR) studies is useful to reduce the need for paramagnetic materials in contrast enhanced MR imaging.

TL;DR: A probabilistic and level set model for three-dimensional medical object extraction is proposed, which is called region competition based active contour, and is fast, convergent, adapted to a broad range of medical objects and produces satisfactory results.

TL;DR: In this paper, a computer program of the Hilbert space-filling curve ordering generated from a tensor product formula is used to rearrange pixels of medical images to enhance pixel locality.

TL;DR: The possibility of using computer analysis of high-resolution CT images to radiologically classify the shape of pulmonary nodules is investigated and two quantitative parameters for characterizing nodules are calculated: circularity and second central moment.

TL;DR: An integrated approach for multi-spectral segmentation of MR images is presented, based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space.

TL;DR: It is concluded that anisotropy has significant effects on peri-implant stress and strain and careful consideration should be given to its use in biomechanical FE studies.

TL;DR: A probabilistic description of the model is defined, that is characterised by a remarkable simplicity, such that its realisation can be easily and efficiently implemented in any high- or low-level programming language, thus allowing it to be run on virtually any kind of platform.

TL;DR: A system based on image processing and classification techniques for the estimation of quantitative parameters to define vessel deformation and the classification of image data into two classes is described, underscoring the value of the proposed system to assist in the detection of early glaucomatous change.

TL;DR: The results indicate that the image processing procedures successfully extracted information from a large 3D dataset of the coronary arterial tree to provide prognostic indications in the form of arterial Tree parameters and anatomical area at risk.

TL;DR: This work compares the accuracy of the Otsu thresholding and a region sampled binary mixture approach, for live mouse LV volume measurement using 100 microm resolution datasets, for micro-CT-based cardiac function estimation in small animals.

TL;DR: A new deformable model that uses the simulation of charged elements to segment medical images and is a promising approach for the segmentation of anatomic structures in a wide variety of medical images across different modalities is indicated.

TL;DR: This work demonstrated the usefulness of locally self-similar random fields with long-range dependence for modelling chromatin condensation and used two-dimensional isotropic generalized Cauchy field to characterize localSelf-similarity and global long- range dependence behaviors in the image spatial data.