background: volume estimation of brain is important for many neurological applications. it is necessary in measuring brain growth and changes in brain in normal/abnormal patients. thus, accurate brain volume measurement is very important. magnetic resonance imaging (mri) is the method of choice for volume quantification due to excellent levels of image resolution and between-tissue contrast. st...

Introduction: The increased importance of automated computer techniques for anatomical brain mapping from MR images and quantitative brain image analysis methods leads to an increased need for validation and evaluation of the effect of image acquisition parameters on performance of these procedures. Validation of analysis techniques of in-vivo acquired images is complicated due to the lack of r...

Medical image segmentation plays a vital role in image processing due to the catering needs of the medical images in automating, delineating anatomical structures and diagnosis. Very often the medical images contain uncertain, vague, and incomplete data definition. The concepts of lower and upper approximations of rough sets effectively handle this data. In this paper, rough sets based clusteri...

This memo describes the performance evaluation of the TINA medical image segmentation algorithm described in Memo 2004-009 when applied to simulated images produced by the Brainweb MRI simulator. In order to allow Monte-Carlo experiments to be performed using independent image noise fields, and to avoid problems introduced by the presence of histogram artefacts in the Brainweb simulated images ...

We present an edge preserving and denoising filter for enhancing the features in images, which contain an ROI having a narrow spatial extent. Typical examples include angiograms, or ROI’s spatially distributed in multiple locations and contained within an outlying region, such as in multiple-sclerosis. The filtering involves determination of multiplicative weights in the spatial domain using an...

In this paper we propose a constrained version of MumfordShah’s[1] segmentationwith an information-theoretic point of view[2] in order to devise a systematic procedure to segment brain MRI data for two modalities of parametric T1-Map and T1-weighted images in both 2-D and 3-D settings. The incorporation of a tuning weight in particular adds a probabilistic avor to our segmentation method, and m...

We present a new method for the fast and robust computation of information theoretic similarity measures for alignment of multi-modality medical images. The proposed method defines a non-uniform, adaptive sampling scheme for estimating the entropies of the images, which is less vulnerable to local maxima as compared to uniform and random sampling. The sampling is defined using an octree partiti...

Image registration is an essential step in creating augmented environments and performing image-guided interventions. Registration algorithms are commonly validated against simulation and real data. Both validations are critical in a comprehensive analysis: On one hand, the simulation data provides ground truth registration results and can therefore accurately measure the performance of algorit...

In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system ...

In this paper, we present an application of Riemannian geometry for processing non-Euclidean image data. We consider the image as residing in a Riemannian manifold, for developing a new method to brain edge detection and brain extraction. Automating this process is a challenge due to the high diversity in appearance brain tissue, among different patients and sequences. The main contribution, in...