Abstract:
In this paper, a new intelligent method of classifying benign
and malignant melanoma lesions is implemented. The system
consists of four stages; image pre-processing, image
segmentation, feature extraction, and image classification. As
the first step of the image analysis, pre-processing techniques
are implemented to remove noise and undesired structures
from the images using techniques such as median filtering
and contrast enhancement. In the second step, a simple
thresholding method is used to segment and localise the
lesion, a boundary tracing algorithm is also implemented to
validate the segmentation. Then, a wavelet approach is used
to extract the features, more specifically Wavelet Packet
Transform (WPT). Finally, the dimensionality of the selected
features is reduced with Principal Component Cnalysis
(PCA) and later supplied to an Artificial Neural Network and
Support Vector Machine classifiers for classification. The
ability to correctly discriminate between benign and
malignant lesions was about 95% for the Artificial Neural
Network and 85% for the Support Vector Machine classifier.
