Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis

Abstract

Non-Melanoma skin cancer is one of the most frequent types of cancer. Early detection is encouraged so as to ensure the best treatment, Hyperspectral imaging is a promising technique for non-invasive inspection of skin lesions, however, the optimal wavelengths for these purposes are yet to be conclusively determined. A visible-near infrared hyperspectral camera with an ad-hoc built platform was used for image acquisition in the present study. Robust statistical techniques were used to conclude an optimal range between 573.45 and 779.88 nm to distinguish between healthy and non-healthy skin. Wavelengths between 429.16 and 520.17 nm were additionally found to be optimal for the differentiation between cancer types.

Fig. 1.

The hyperspectral pushbroom platform (80 × 25 cm) and system built for data acquisition purposes in the present study. (a & b) Multifunctional structure, composed of the sensor, halogen light illumination, and calibration marker board and frame. (c) Electronic module controller. (d) Power supply connected to the controller.

Fig. 2.

The marker board used for the calibration and correction of the hyperspectral images. (a) Spectralon reflectance pattern. (b) Region (marked in red) used to obtain reflectance values for all 270 bands of the camera

Fig. 3.

Examples of sampled hyperspectral curves from different patients. (a) A graphical description of the image acquisition workflow. (b) An example of (upper) Basal Cell Carcinoma, found under the hairline of the frontal portion of a male patient’s head, and (lower) a Squamous Cell Carcinoma found on the back of a female patient’s hand. (c) Examples of the hyperspectral signatures obtained for (upper) the Basal Cell Carcinoma patient (a-upper) and the Squamous Cell Carcinoma patient (a-lower).ç. Faces and distinguishing features have been excluded from these figures to ensure patient confidentiality.

Fig. 4.

Graphs presenting the logarithm of Shapiro-Wilks p-values as well as test statistics (w) for each of the samples across each of the bands. The solid horizontal line in each of the left-hand panels mark the p = log(0.003) threshold, that is, all log(p) values that fall below this line have less than a 5% chance of being false positives, and can thus be considered strong deviations from the normal distribution.

Fig. 5.

Graphs presenting p(H₀) calibrations for each of the Shapiro-Wilks p-values in Fig. 4. Central values were calculated using 1:2 prior probabilities while confidence intervals mark upper bound 3:10 prior probabilities in favour of H₀ and lower bound 7:10 prior probabilities in favour of H₀.

Fig. 6.

Calculated residuals for fitted linear models across the entire spectrum analysed.

Fig. 7.

Sample skewness and kurtosis calculations across the entire spectrum analysed.

Fig. 8.

Hyperspectral signatures for each of the samples. (a) Robust signature marking the central tendency as well as 5% and 95% quantile confidence intervals (lower lines and upper lines respectively). (b) √BWMV calculations representing robust sample variance.

Fig. 9.

Univariate hypotheses test results comparing each of the samples across each of the hyperspectral bands using the Levene test for homoscedasticity. (a) Probability of Null Hypotheses (P(H₀)) values, calibrated for each p-value using priors of 1:2 to mark the central tendency, while confidence intervals mark upper bounds using 3:10 prior probabilities in favour of H₀ and lower bounds using 7:10 prior probabilities in favour of H₀. (b) Test statistic calculations for each of the corresponding hypothesis tests.

Fig. 10.

Comparisons of samples using calculations of distribution similarities via Jensen-Shannon distance metricss.

Fig. 11.

Optimally defined windows as established by multiple methods within the present study. Area delimited by dotted vertical lines marks the final window of interest between 573.45 and 779.88 nm.

Fig. 12.

Visualisation of different skin lesions via single bands (611.18 nm and 735.49 nm) of hyperspectral images, as well as their corresponding RGB images. Channel bandwidths have been measured at 2.2 nm.