Overview of Advanced Computer Vision Systems for Skin Lesions Characterization IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 13, NO. 5, SEPTEMBER 2009 Ilias Maglogiannis, Member, IEEE, and Charalampos N. Doukas, Student Member, IEEE Presentor: 陳麒文

Outline Skin cancer back- Ground information Materials and methods Image Acquisition Techniques Definition of Features for the Classification of Skin Lesions Skin lesion classification methods Results

Definition of Features for the Classification of Skin Lesions ABCD Rule pattern analysis Menzies method seven-point checklist; texture analysis

ABCD rules asymmetry border color differential structures

Pattern analysis Pattern analysis Menzies method Menzies method Seven-point check list Seven-point check list atypical pigment network, blue-whitish veil, atypical vascular pattern irregular streaks, irregular dots/globules, irregular blotches, and regression structures Texture analysis Texture analysis

SKIN LESION CLASSIFICATION METHODS Learning Phase statistical Neural networks support vector machine (SVM) adaptive wavelet-transform-based tree-structure classification (ADWAT) Testing Phase

Feature selection The success of image recognition depends on the correct selection of the features => optimization problem heuristic strategies, greedy or genetic algorithms strategies from statistical pattern recognition XVAL, LOO, SFFS, SBFS, PCA, GSFS

RESULTS FROM EXISTING SYSTEMS

Conclusion It is often difficult to differentiate early melanoma from other benign skin lesions even for experienced It is even more difficult for primary care physicians and general practitioners The early diagnosis of skin cancer is important for the therapeutic procedure and reducing mortality rates. Most remarkable features have been surveyed in this paper Cost of a simple CDSS for skin assessment is low Standardization of all steps in the CDSS procedure from the image acquisition until the feature extraction and the classification stages is considered essential

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