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

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Semi-automatic Segmentation of Liver Tumors from CT Scans Using Bayesian Rule-based 3D Region Growing
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 [...]

Coronary Artery Tracking in 3D Cardiac CT Images Using Local Morphological Reconstruction Operators
No opinion
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 [...]

Contrast Enhancement for Liver Tumor Identification
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 [...]

3D Interactive Centerline Extraction
No opinion
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
No opinion
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 [...]

Semi-automatic Segmentation of 3D Liver Tumors from CT Scans Using Voxel Classification and Propagational Learning
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 [...]

Cognition Network Technology for a Fully Automated 3D Segmentation of Liver Tumors
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 [...]

A semi-automated method for liver tumor segmentation based on 2D region growing with
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. [...]

Shape and Appearance Models for Automatic Coronary Artery Tracking
No opinion
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 [...]

An entropy based multi-thresholding method for semi-automatic segmentation of liver tumors
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 [...]

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