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jClustering, an open framework for the development of 4D clustering algorithms.

Mateos-Pérez JM, García-Villalba C, Pascau J, Desco M, Vaquero JJ - PLoS ONE (2013)

Bottom Line: Providing an easily extensible open tool encourages publication of source code to facilitate the process of comparing algorithms and provide interested third parties with the opportunity to review code.The internal structure of the framework allows an external developer to implement new algorithms easily and quickly, focusing only on the particulars of the method being implemented and not on image data handling and preprocessing.Both binary packages and source code have been published, the latter under a free software license (GNU General Public License) to allow modification if necessary.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain ; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.

ABSTRACT
We present jClustering, an open framework for the design of clustering algorithms in dynamic medical imaging. We developed this tool because of the difficulty involved in manually segmenting dynamic PET images and the lack of availability of source code for published segmentation algorithms. Providing an easily extensible open tool encourages publication of source code to facilitate the process of comparing algorithms and provide interested third parties with the opportunity to review code. The internal structure of the framework allows an external developer to implement new algorithms easily and quickly, focusing only on the particulars of the method being implemented and not on image data handling and preprocessing. This tool has been coded in Java and is presented as an ImageJ plugin in order to take advantage of all the functionalities offered by this imaging analysis platform. Both binary packages and source code have been published, the latter under a free software license (GNU General Public License) to allow modification if necessary.

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k-means++ result example.Clustering results for a 13NH3 pig study using a k-means++ algorithm (k = 10). The myocardium, the right ventricle, the left ventricle, and the lungs are clearly delineated. The activity curves for some relevant regions (right ventricle, left ventricle, and myocardium; right panel) are plotted from the text file stored by jClustering after segmentation. The left image shows the last frame of the pseudo-dynamic structure generated to display the results. The value of each voxel in this image contains the cluster number that contains said voxel. Each frame before the last one displays only the voxels that belong to that cluster number.
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pone-0070797-g004: k-means++ result example.Clustering results for a 13NH3 pig study using a k-means++ algorithm (k = 10). The myocardium, the right ventricle, the left ventricle, and the lungs are clearly delineated. The activity curves for some relevant regions (right ventricle, left ventricle, and myocardium; right panel) are plotted from the text file stored by jClustering after segmentation. The left image shows the last frame of the pseudo-dynamic structure generated to display the results. The value of each voxel in this image contains the cluster number that contains said voxel. Each frame before the last one displays only the voxels that belong to that cluster number.

Mentions: Figure 4 shows a segmentation of a dynamic PET study using a k-means++ algorithm (k = 10) with Euclidean distance as a metric. The image dimensions are 128×128×47, 25 frames, and the total time used in the segmentation is 20.15 seconds. Several principal components from a PCA of this image are shown in Figure 5; a total of 25 principal components were computed in 14.20 seconds. Figure 6 shows a simple segmentation of a dynamic human MRI study with gadolinium as a contrast agent using a grayscale LUT. The image dimensions are 128×128×28, 40 frames, and the total processing time is 10.47 seconds.


jClustering, an open framework for the development of 4D clustering algorithms.

Mateos-Pérez JM, García-Villalba C, Pascau J, Desco M, Vaquero JJ - PLoS ONE (2013)

k-means++ result example.Clustering results for a 13NH3 pig study using a k-means++ algorithm (k = 10). The myocardium, the right ventricle, the left ventricle, and the lungs are clearly delineated. The activity curves for some relevant regions (right ventricle, left ventricle, and myocardium; right panel) are plotted from the text file stored by jClustering after segmentation. The left image shows the last frame of the pseudo-dynamic structure generated to display the results. The value of each voxel in this image contains the cluster number that contains said voxel. Each frame before the last one displays only the voxels that belong to that cluster number.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3750055&req=5

pone-0070797-g004: k-means++ result example.Clustering results for a 13NH3 pig study using a k-means++ algorithm (k = 10). The myocardium, the right ventricle, the left ventricle, and the lungs are clearly delineated. The activity curves for some relevant regions (right ventricle, left ventricle, and myocardium; right panel) are plotted from the text file stored by jClustering after segmentation. The left image shows the last frame of the pseudo-dynamic structure generated to display the results. The value of each voxel in this image contains the cluster number that contains said voxel. Each frame before the last one displays only the voxels that belong to that cluster number.
Mentions: Figure 4 shows a segmentation of a dynamic PET study using a k-means++ algorithm (k = 10) with Euclidean distance as a metric. The image dimensions are 128×128×47, 25 frames, and the total time used in the segmentation is 20.15 seconds. Several principal components from a PCA of this image are shown in Figure 5; a total of 25 principal components were computed in 14.20 seconds. Figure 6 shows a simple segmentation of a dynamic human MRI study with gadolinium as a contrast agent using a grayscale LUT. The image dimensions are 128×128×28, 40 frames, and the total processing time is 10.47 seconds.

Bottom Line: Providing an easily extensible open tool encourages publication of source code to facilitate the process of comparing algorithms and provide interested third parties with the opportunity to review code.The internal structure of the framework allows an external developer to implement new algorithms easily and quickly, focusing only on the particulars of the method being implemented and not on image data handling and preprocessing.Both binary packages and source code have been published, the latter under a free software license (GNU General Public License) to allow modification if necessary.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain ; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.

ABSTRACT
We present jClustering, an open framework for the design of clustering algorithms in dynamic medical imaging. We developed this tool because of the difficulty involved in manually segmenting dynamic PET images and the lack of availability of source code for published segmentation algorithms. Providing an easily extensible open tool encourages publication of source code to facilitate the process of comparing algorithms and provide interested third parties with the opportunity to review code. The internal structure of the framework allows an external developer to implement new algorithms easily and quickly, focusing only on the particulars of the method being implemented and not on image data handling and preprocessing. This tool has been coded in Java and is presented as an ImageJ plugin in order to take advantage of all the functionalities offered by this imaging analysis platform. Both binary packages and source code have been published, the latter under a free software license (GNU General Public License) to allow modification if necessary.

Show MeSH