Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/65093
Title: Image classification of skin moles & melanomas
Authors: Luo, Ying
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2014
Abstract: With the rapid development of image processing technologies, melanoma recognition system is gaining popularity both in research and medical purposes. The aim of this study is to investigate a good method to differentiate malignant melanomas from moles. In the recognition system, image segmentation should be done as a pre-processing step. Then morphological operation is applied to localize potential melanoma boundary regions. Next, discrimination features which provide good discrimination of malignant melanomas from moles are extracted. Finally, the selected features are applied to a neural network classifier to classify the skin lesion as melanoma or mole. With our approach, we obtained 82% correct classification rate in a dataset consisting of 100 images (50 moles and 50 melanomas) downloaded from DermQuest[1] website.
URI: http://hdl.handle.net/10356/65093
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
LUO_YING_2014.pdf
  Restricted Access
6.44 MBAdobe PDFView/Open

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.