Detail publikačního výsledku

Automated skin lesion segmentation using K-Means clustering from digital dermoscopic images

AGARWAL, A.; ISSAC, A.; DUTTA, M.; ŘÍHA, K.; UHER, V.

Originální název

Automated skin lesion segmentation using K-Means clustering from digital dermoscopic images

Anglický název

Automated skin lesion segmentation using K-Means clustering from digital dermoscopic images

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Melanoma can prove fatal if not diagnosed at early stage. The accuracy of identification of skin cancer from dermoscopic images is directly proportional to the accuracy of the skin lesion segmentation. This work proposes a skin lesion segmentation method using clustering technique. The use of smoothing filter and area thresholding is competent enough to sufficiently reject the noisy pixels from the finally segmented image. The results of skin lesion segmentation obtained from the proposed algorithm has been compared with the annotated images. The results have been expressed in the form of overlapping score and correlation coefficient. The maximum values of overlapping score and correlation coefficient obtained from the algorithm are 96.75% and 97.66% respectively. The results are convincing and suggests that the proposed work can be used for some real time application.

Anglický abstrakt

Melanoma can prove fatal if not diagnosed at early stage. The accuracy of identification of skin cancer from dermoscopic images is directly proportional to the accuracy of the skin lesion segmentation. This work proposes a skin lesion segmentation method using clustering technique. The use of smoothing filter and area thresholding is competent enough to sufficiently reject the noisy pixels from the finally segmented image. The results of skin lesion segmentation obtained from the proposed algorithm has been compared with the annotated images. The results have been expressed in the form of overlapping score and correlation coefficient. The maximum values of overlapping score and correlation coefficient obtained from the algorithm are 96.75% and 97.66% respectively. The results are convincing and suggests that the proposed work can be used for some real time application.

Klíčová slova

Dermoscopic Image; Skin Lesion; K-means Clustering; Intensity Threshold; Mathematical Morphology

Klíčová slova v angličtině

Dermoscopic Image; Skin Lesion; K-means Clustering; Intensity Threshold; Mathematical Morphology

Autoři

AGARWAL, A.; ISSAC, A.; DUTTA, M.; ŘÍHA, K.; UHER, V.

Rok RIV

2018

Vydáno

05.07.2017

Nakladatel

IEEE

Místo

Barcelona, Španělsko

ISBN

978-1-5090-3982-1

Kniha

International Conference on Telecommunications and Signal Processing (TSP)

Strany od

743

Strany do

748

Strany počet

6

URL

http://ieeexplore.ieee.org/document/8076087/

BibTex

@inproceedings{BUT141355,
  author="Ashi {Agarwal} and Ashish {Issac} and Malay Kishore {Dutta} and Kamil {Říha} and Václav {Uher}",
  title="Automated skin lesion segmentation using K-Means clustering from digital dermoscopic images",
  booktitle="International Conference on Telecommunications and Signal Processing (TSP)",
  year="2017",
  pages="743--748",
  publisher="IEEE",
  address="Barcelona, Španělsko",
  doi="10.1109/TSP.2017.8076087",
  isbn="978-1-5090-3982-1",
  url="http://ieeexplore.ieee.org/document/8076087/"
}