Comparative study of maximum likelihood and spectral angle mapper algorithms used for automated detection of melanoma.

BACKGROUND: Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous if it cannot be found or detected early. Only biopsy can determine exact malformation diagnosis, although it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope.

METHODS: In this paper, we provide a new approach using methods known as 'imaging spectroscopy' or 'spectral imaging' for early detection of melanoma using two different supervised classifier algorithms, maximum likelihood (ML) and spectral angle mapper (SAM). SAM rests on the spectral 'angular distances' and the conventional classifier ML rests on the spectral distance concept.

RESULTS AND CONCLUSIONS: The results show that the ML classifier was more efficient for pixel classification than SAM. However, SAM was more suitable for object classification.