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

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Model-based left ventricle segmentation in 3D ultrasound using phase image
No opinion
In this paper, we propose a semi-automatic method for left ventricle segmentation. The proposed method utilizes a multi-scale quadrature filter method to enhance the 3D volume, followed by a model-based level set method to segment the endocardial surface of [...]

Segmentation of Multi-Center 3D Left Ventricular Echocardiograms by Active Appearance Models
No opinion
Segmentation of 3D echocardiograms (3DEs) is still a challenging task due to the low signal-to-noise ratio, the limited field of view, and typical ultrasound artifacts. We propose to segment the left ventricular endocardial surface by using Active Appearance [...]

Real-time Tracking of the Left Ventricle in 3D Ultrasound Using Kalman Filter and Mean Value Coordinates
No opinion
A method for real-time automatic tracking of the left ventricle (LV) in 3D ultrasound is presented. A mesh model of the LV is deformed using mean value coordinates enabling large variations. Kalman filtering and edge detection is used to track the mesh in [...]

Learning Shape Representations for Multi-Atlas Endocardium Segmentation in 3D Echo Images
No opinion
As part of the CETUS challenge, we present a multi-atlas segmentation framework to delineate the left-ventricle endocardium in echocardiographic images. To increase the robustness of the registration step, we introduce a speckle reduction step and a new shape [...]

Left Ventricle Segmentation in Cardiac Ultrasound Using Hough-Forests With Implicit Shape and Appearance Priors
No opinion
We propose a learning based approach to perform automatic segmentation of the left ventricle in 3D cardiac ultrasound images. The segmentation contour is estimated through the use of a variant of Hough forests whose object localization capabilities are [...]

Endocardial 3D Ultrasound Segmentation using Autocontext Random Forests
No opinion
In this paper, we present the use of a generic image segmentation method, namely a succession of Random Forest classifiers in an autocontext framework, for the MICCAI 2014 Challenge on Endocardial 3D Ultrasound Segmentation (CETUS). The proposed method [...]

Endocardial Segmentation using Structured Random Forests in 3D Echocardiography
No opinion
Segmentation of the left ventricle endocardium in 3D echocardiography is a critical step for the diagnosis of heart disease. Although recent work has shown effective endocardial edge detection, these techniques still preserve spurious anatomical edge [...]

Automatized Evaluation of the Left Ventricular Ejection Fraction from Echocardiographic Images Using Graph Cut
No opinion
In this paper, we present a fast and interactive graph cut method for 3D segmentation of the endocardial wall of the left ventricle (LV) given 3D echocardiographic images. This is a challenging task due to the poor contrast and the low signal-to-noise ratio [...]

Fast Tracking of the Left Ventricle Using Global Anatomical Affine Optical Flow and Local Recursive Block Matching
No opinion
We present a novel method for segmentation and tracking of the left ventricle (LV) in 4D ultrasound sequences using a combination of automatic segmentation at the end-diastolic frame and tracking using both a global optical flow-based tracker and local block [...]

Clinical Expert Delineation of 3D Left Ventricular Echocardiograms for the CETUS Segmentation Challenge
No opinion
Within the framework of the CETUS challenge, forty-five 3D echocardiographic datasets have been acquired and segmented independently by three clinical experts from different hospitals. The goal was to generate a well-established ground truth of validated [...]

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Publication of the Month
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Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach
by Lowekamp B., Chen D., Yaniv Z., Yoo T.

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