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:
-Open-access to articles and reviews
-Open peer-review that invites discussion between reviewers and authors
-Support for continuous revision of articles, code, and reviews
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-Open-access to articles and reviews
-Open peer-review that invites discussion between reviewers and authors
-Support for continuous revision of articles, code, and reviews
Subscribe to the Kitware's newsletter
to receive news about open-source on your desk, it's free!
Purpose: Recent years have seen growing interest in adaptive radiation therapy (RT), but the existing software tools are not ideal for research use, as they are either expensive and closed proprietary applications or free open-source packages with limited [...]
In this work, we try to develop a fast converging method for
segmentation assisted deformable registration. The segmentation step
consists of a piece-wise constant Mumford-Shah energy model while reg-
istration is driven by the sum of squared distances of [...]
In radiation treatment (RT) planning medical doctors need to consider a variety of information sources for anatomical and functional target volume delineation. The validation and inspection of the defined target volumes and the resulting RT plan is a complex [...]
In radiotherapy (RT) for tumor delineation and diagnostics, complementary information of multi-modal images is used. Using high ionizing radiation, the accuracy of registered volume data is crucial; therefore a reliable and robust evaluation method for [...]
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 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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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