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Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images

Overview of attention for article published in BMC Bioinformatics, October 2010
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1 Redditor

Citations

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Title
Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images
Published in
BMC Bioinformatics, October 2010
DOI 10.1186/1471-2105-11-s6-s26
Pubmed ID
Authors

Sinan Kockara, Mutlu Mete, Bernard Chen, Kemal Aydin

Abstract

Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density--greater than certain number of points--around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 6%
Brazil 1 6%
Unknown 14 88%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 25%
Researcher 2 13%
Lecturer > Senior Lecturer 1 6%
Professor 1 6%
Student > Doctoral Student 1 6%
Other 2 13%
Unknown 5 31%
Readers by discipline Count As %
Computer Science 4 25%
Medicine and Dentistry 2 13%
Neuroscience 2 13%
Agricultural and Biological Sciences 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Other 2 13%
Unknown 4 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 May 2013.
All research outputs
#20,194,150
of 22,711,242 outputs
Outputs from BMC Bioinformatics
#6,831
of 7,259 outputs
Outputs of similar age
#93,949
of 99,125 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 50 outputs
Altmetric has tracked 22,711,242 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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