Name
Abstract | ||
|---|---|---|
This paper presents a wavelet-based texture analysis method for classification of melanoma. The method applies tree-structured wavelet transform on different color channels of red, green, blue and luminance of dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. Feature extraction and a two-stage feature selection method, based on entropy and correlation, were applied to a train set of 103 images. The resultant feature subsets were then fed into four different classifiers: support vector machine, random forest, logistic model tree and hidden naive bayes to classify melanoma in a test set of 102 images, which resulted in an accuracy of 88.24% and ROC area of 0.918. Comparative study carried out in this paper shows that the proposed feature extraction method outperforms three other wavelet-based approaches. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/DICTA.2010.22 | Digital Image Computing: Techniques and Applications |
Keywords | Field | DocType |
different classifier,feature extraction,wavelet-based approach,wavelet-based texture analysis,roc area,wavelet-based texture analysis method,proposed feature extraction method,resultant feature subsets,wavelet coefficient,different color channel,two-stage feature selection method,wavelet,wavelet transform,comparative study,red green blue,naive bayes,random forest,logistic model tree,support vector machines,entropy,classification,logistic model,color channels,image texture,accuracy,tree structure,wavelet transforms,statistical analysis,feature selection,wavelet analysis,support vector machine | Computer vision,Pattern recognition,Naive Bayes classifier,Feature selection,Image texture,Computer science,Logistic model tree,Feature extraction,Artificial intelligence,Random forest,Wavelet,Wavelet transform | Conference |
ISBN | Citations | PageRank |
978-0-7695-4271-3 | 4 | 0.56 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rahil Garnavi | 1 | 222 | 20.95 |
| Mohammad Aldeen | 2 | 123 | 16.39 |
| James Bailey | 3 | 47 | 6.10 |