Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images.
BMC Bioinformatics
; 11 Suppl 6: S26, 2010 Oct 07.
Article
in En
| MEDLINE
| ID: mdl-20946610
ABSTRACT
BACKGROUND:
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.RESULTS:
Each approach is examined on a set of 100 dermoscopy images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates; false positives and false negatives along with true positives and true negatives are quantified by comparing results with manually determined borders from a dermatologist. The assessments obtained from both methods are quantitatively analyzed over three accuracymeasures:
border error, precision, and recall.CONCLUSION:
As well as low border error, high precision and recall, visual outcome showed that the DBSCAN effectively delineated targeted lesion, and has bright future; however, the FCM had poor performance especially in border error metric.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Image Interpretation, Computer-Assisted
/
Dermoscopy
Type of study:
Diagnostic_studies
Limits:
Humans
Language:
En
Journal:
BMC Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2010
Document type:
Article
Affiliation country:
United States