Analysis and Classification of Skin Lesions Using 3D Volume Reconstruction

3D volume reconstruction is used to identify the skin cancer at earlier stage. Through the person may be preventing from death. The subsurface information such as blood layer thickness and blood volume components can be identifying by multispectral transillumination images of the skin lesions. Lesions are simulated by using volume reconstruction, thus the result shows excellent volume with accuracy rate. Preliminary validation is also done by the small set of clinical lesions. The lesion severity can be categorized by an expert dermatologist using two features average blood layer thickness and ratio of blood volume to total lesion volume. The lesion can be classified into three types moderate, mild and severe class with high accuracy. However, these methods do not perform efficiently over a large set of images. An inverse volume reconstruction method is presented which uses a genetic algorithm optimization procedure with a novel population initialization routine and nudge operator based on the multispectral images to reconstruct the melanin and blood layer volume components. In our system, it is expected to have the ability to differentiate classes of lesions’ severity based on multispectral trans-illumination and it can be perform over a large set of images. Therefore it has features like fast screening, tracking and detection of early skin cancers such as melanoma.