The MIDAS Journal is an Open Access on-line publication covering different domains from Visualization to Image processing.

The unique characteristics of the MIDAS Journal include:

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3D Segmentation In The Clinic: A Grand Challenge II at MICCAI 2008 - MS Lesion Segmentation
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This document examines the application of a new parametric method on the segmentation of MS lesions in brain sMRI, as applied to the data provided for the MS Lesion Segmentation Challenge at MICCAI 2008. The method uses the vector image joint histogram, [...]

Multiple Sclerosis Lesion Segmentation Using Statistical and Topological Atlases
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This paper presents a new fully automatic method for segmentation of brain images that possess multiple sclerosis (MS) lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its [...]

A robust Expectation-Maximization algorithm for Multiple Sclerosis lesion segmentation
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A fully automatic workflow for Multiple Sclerosis (MS) lesion segmentation is described. Fully automatic means that no user interaction is performed in any of the steps and that all parameters are fixed for all the images processed in beforehand. Our workflow [...]

Liver Tumor segmentation in CT images using probabilistic
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Liver tumors segmentation is an important prerequisite for planning of surgical interventions. For clinical applicability, the segmentation approach must be able to cope with the high variation in shape and gray-value appearance of the liver. We present a [...]

Multiple Sclerosis Detection in Multispectral Magnetic Resonance Images with Principal Components Analysis.
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This paper presents a local feature vector based method for automated Multiple Sclerosis (MS) lesion segmentation of multi spectral MRI data. Twenty datasets from MS patients with FLAIR, T1,T2, MD and FA data with expert annotations are available as training [...]

3D Segmentation in the Clinic: A Grand Challenge II - Coronary Artery Tracking
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In this paper the Coronary Artery Tracking competition, which was part of the workshop: "3D Segmentation in the Clinic: A Grand Challenge II" is described. This workshopwas held during the 2008 Medical Image Computing and Computer Assisted Intervention [...]

Coronary Artery Tracking in 3D Cardiac CT Images Using Local Morphological Reconstruction Operators
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Automatic segmentation and tracking of the coronary artery tree from Cardiac Multislice-CT images is an important goal to improve the diagnosis and treatment of coronary artery disease. This paper presents a semi-automatic algorithm (one input point per [...]

Automatic Coronary Tree Modeling
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In this paper, we present an automatic method for extracting center axis representations (centerlines) of coronary arteries in contrast enhanced (CE)-CT angiography scans. The algorithm first detects the aorta which is used as an initial mask for [...]

Coronary Centerline Extraction Using Multiple Hypothesis Tracking and Minimal Paths
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This paper describes an interactive approach to the identification of coronary arteries in 3D angiography images. The approach is based on a novel multiple hypothesis tracking methodology which is complemented with a standard minimal path search, and it [...]

Contrast Enhancement for Liver Tumor Identification
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In CT images, tumors located in a liver are generally identified by intensity difference between tumor and liver. The intensity of the tumor can be lower and or higher than that of the liver. However, the main problem of liver tumor detection from is related [...]

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OpenTissue - An Open Source Toolkit for Physics-Based Animation
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Computational Algorithms
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