Tree Structured Model of Skin Lesion Growth Pattern via Color Based Cluster Analysis
This paper presents a novel approach to analysis and classification of skin lesions based on their growth pattern. Our method constructs a tree structure for every lesion by repeatedly subdividing the image into sub-images using color based clustering. In this method, segmentation which is a challenging task is not required. The obtained multi-scale tree structure provides a framework that allows us to extract a variety of features, based on the appearance of the tree structure or sub-images corresponding to nodes of the tree. Preliminary features (the number of nodes, leaves, and depth of the tree, and 9 compactness indices of the dark spots represented by the sub-images associated with each node of the tree) are used to train a supervised learning algorithm. Results show the strength of the method in classifying lesions into malignant and benign classes. We achieved Precision of 0.855, Recall of 0.849, and F-measure of 0.834 using 3-layer perceptron and Precision of 0.829, Recall of 0.832, and F-measure of 0.817 using AdaBoost on a dataset containing 112 malignant and 298 benign lesion dermoscopic images.
KeywordsTree Structure Dark Spot Dark Pixel Cluster Stage Preliminary Feature
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- 1.Argenziano, G., Soyer, H.P., et al.: Interactive Atlas of Dermoscopy (Book and CD-ROM). Edra Medical Publishing and New Media (2000)Google Scholar
- 3.Betta, G., Di Leo, G., Fabbrocini, G., Paolillo, A., Scalvenzi, M.: Automated application of the 7-point checklist diagnosis method for skin lesions: Estimation of chromatic and shape parameters. In: IEEE Instrumentation and Measurement Technology Conference, vol. 3, pp. 1818–1822 (2005)Google Scholar
- 5.Clark, W.H., Ainsworth, A.M., Bernardino, E.A., Yang, C.H., Mihm, C.M., Reed, R.J.: The developmental biology of primary human malignant melanomas. Semin. Oncol. 2(1), 83–103 (1975)Google Scholar
- 7.Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Amsterdam (2001)Google Scholar
- 13.Zhou, H., Chen, M., Zou, L., Gass, R., Ferris, L., Drogowski, L., Rehg, J.: Spatially constrained segmentation of dermoscopy images. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2008, pp. 800–803 (May 2008)Google Scholar