Lesion classification using 3D skin surface tilt orientation.
OBJECTIVE
Current non-invasive diagnostic procedures to detect skin cancer rely on two-dimensional (2D) views of the skin surface. For example, the most commonly-used ABCD features are extracted from the 2D images of skin lesion. However, because the skin surface is an object in three-dimensional (3D) space, valuable additional information can be obtained from a perspective of 3D skin objects. The aim of this work is to discover the new diagnostic features by considering 3D views of skin artefacts.


METHODS
A surface tilt orientation parameter was proposed to quantify the skin and the lesion in 3D space. The skin pattern was first extracted from simply captured white light optical clinical (WLC) skin images by high-pass filtering. Then the directions of the projected skin lines were determined by skin pattern analysis. Next the surface tilt orientations of skin and lesion were estimated using the shape from texture technique. Finally the difference of tilt orientation in the lesion and normal skin areas, combined with the ABCD features, was used as a lesion classifier.


RESULTS
The proposed method was validated by processing a set of images of malignant melanoma and benign naevi. The scatter plot of classification using the feature of surface tilt orientation alone showed the potential of the new 3D feature, enclosing an area of 0.78 under the ROC curve. The scatter plot of classification, combining the new feature with the ABCD features by use of Principal Component Analysis (PCA), demonstrated an excellent separation of benign and malignant lesions. An ROC plot for this case enclosed an area of 0.85. Compared with the ABCD analysis where the area under the ROC curve was 0.65, it indicated that the surface tilt orientation (3D information) was able to enhance the classification results significantly.


CONCLUSIONS
The initial classification results show that the surface tilt orientation has a potential to increase lesion classifier accuracy. Combined with the ABCD features, it is very promising to distinguish malignant melanoma from benign lesions.
Institute of Arts, Science & Technology, Glyndwr University, Wrexham, UK. z.she@glyndwr.ac.uk
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D000465:Algorithms; D016477:Artifacts; D046169:Dermoscopy / Q000379:methods* / Q000592:standards; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods* / Q000592:standards; D008545:Melanoma / Q000145:classification / Q000473:pathology*; D008954:Models, Biological; D009369:Neoplasms / Q000145:classification / Q000473:pathology; D009506:Nevus / Q000145:classification / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000592:standards; D025341:Principal Component Analysis; D012372:ROC Curve; D015203:Reproducibility of Results; D012867:Skin / Q000473:pathology; D012878:Skin Neoplasms / Q000145:classification / Q000473:pathology*