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

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-Open-access to articles and reviews
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Semi-automatic Segmentation of Liver Tumors from CT Scans Using Bayesian Rule-based 3D Region Growing
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Automatic segmentation of liver tumorous regions often fails due to high noise and large variance of tumors. In this work, a semi-automatic algorithm is proposed to segment liver tumors from computed tomography (CT) images. To cope with the variance of [...]

An entropy based multi-thresholding method for semi-automatic segmentation of liver tumors
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Liver cancer is the fifth most commonly diagnosed cancer and the third most common cause of death from cancer worldwide. A precise analysis of the lesions would help in the staging of the tumor and in the evaluation of the possible applicable therapies. In [...]

Semi-automatic Segmentation of 3D Liver Tumors from CT Scans Using Voxel Classification and Propagational Learning
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A semi-automatic scheme was developed for the segmentation of 3D liver tumors from computed tomography (CT) images. First a support vector machine (SVM) classifier was trained to extract tumor region from one single 2D slice in the intermediate part of a [...]

A semi-automated method for liver tumor segmentation based on 2D region growing with
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Liver tumour segmentation from computed tomography (CT) scans is a challenging task. A semi-automatic method based on 2D region growing with knowledge-based constraints is proposed to segment lesions from constituent 2D slices obtained from 3D CT images. [...]

3D Interactive Centerline Extraction
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This document describes a user-steered method to interactively track centerlines of tubular objects in 3D space. The method is developed as a plug-in of ImageJ using Java language. To evaluate the tracking ability and tracking accuracy, this method has been [...]

Coronary Artery Centerline Tracking Using Axial Symmetries
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We present a method for tracking a coronary artery centerline given a single user supplied distal endpoint. Briefly, we first isolate the aorta and compute its surface. Next, we apply a novel two-stage Hough-like election scheme to the image volume to detect [...]

Shape and Appearance Models for Automatic Coronary Artery Tracking
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Automatic tracking of coronary arteries in Computed Tomography Angiography (CTA) is a challenging task. To accomplish it we propose a method consisting of two main steps: (1) A 3D model of the heart is matched for detecting the approximate position of the [...]

Cognition Network Technology for a Fully Automated 3D Segmentation of Liver Tumors
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The Definiens Cognition Network Technology is applied to detect automatically tumors in a human liver. On the basis of a test data set containing ten tumors we show first quantitative results which are compared to manual segmentations provided by medical [...]

Segmentation of Liver Metastases Using a Level Set Method with Spiral-Scanning Technique and Supervised Fuzzy Pixel Classification
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In this paper a specific method is presented to facilitate the semi-automatic segmentation of liver metastases in CT images. Accurate and reliable segmentation of tumors is e.g. essential for the follow-up of cancer treatment. The core of the algorithm is a [...]

Vessel tracking by connecting the dots
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We propose an algorithm for tracking blood vessel segments in Computed Tomographic (CT) images. Our procedure first finds core points that tend to concentrate along the centerlines of vessels. Intuitively, the core points are centers of intensity plateaus [...]

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Boolean Operations on Surfaces in VTK Without External Libraries
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