Diagnosis of benign and malign skin lesions is currently done mostly relying on visual assessment and frequent biopsies
performed by dermatologists. As the timely and correct diagnosis of these skin lesions is one of the most important
factors in the therapeutic outcome, leveraging new technologies to assist the dermatologist seems natural. Optical
spectroscopy is a technology that is being established to aid skin lesion diagnosis, as the multi-spectral nature of this
imaging method allows to detect multiple physiological changes like those associated with increased vasculature, cellular
structure, oxygen consumption or edema in tumors. However, spectroscopy data is typically very high dimensional (on
the order of thousands), which causes difficulties in visualization and classification. In this work we apply different
manifold learning techniques to reduce the dimensions of the input data and get clustering results. Spectroscopic data of
48 patients with suspicious and actually malignant lesions was analyzed using ISOMAP, Laplacian Eigenmaps and
Diffusion Maps with varying parameters and compared to results using PCA. Using optimal parameters, both ISOMAP
and Laplacian Eigenmaps could cluster the data into suspicious and malignant with 96% accuracy, compared to the
diagnosis of the treating physicians.
Cutaneous T-Cell Lymphoma (CTCL) is a cancer type externally characterized by alterations in the coloring of skin.
Optical spectroscopy has been proposed for quantification of minimal changes in skin offering itself as an interesting tool
for monitoring of CTCL in real-time. However, in order to be used in a valid way, measurements on the lesions have to
be taken at the same position and with the same orientation in each session. Combining hand-held optical spectroscopy
devices with tracking and acquiring synchronously spectral information with position and orientation, we introduce a
novel computer-assisted scheme for valid spectral quantification of disease progression. We further present an
implementation for an augmented reality guidance system that allows to find a point previously analyzed with an
accuracy of 0.8[mm] and 5.0[deg] (vs. 1.6[mm] and 6.6[deg] without guidance). The intuitive guidance, as well as the
preliminary results shows that the presented approach has great potential towards innovative computer-assistance
methods for quantification of disease progression.
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