Itch is the primary symptom of inflammatory skin diseases such as atopic eczema and psoriasis, chronic renal failure, and chronic hepatic failure. Itch, like pain, is a subjective symptom. Characterizing itchy skin and skin prone to itch will lead to better understanding of these symptoms and ultimately better diagnosis and treatment of the underlining disease. The goal of our study is to determine whether the itchy skin region can be detected by hyperspectral imaging. We used an imaging system equipped with liquid crystal tunable filter for collecting hyperspectral images. A halogen lamp was used to illuminate the region of interest. Images were taken from 650 nm to 1100 nm wavelength with 10 nm interval. The hyperspectral images were collected from the forearms of two male and two female subjects. An approximate 50 mm × 50 mm region of interest was marked on the forearms before imaging. The itch was mechanically induced. Imaging was performed for three conditions with a 99% Spectralon white diffuse reflectance target on the side: before inducing itch (normal region), after inducing itch (test region), and after removing itch (control region). Two methods were used to detect the itchy and nonitchy regions from the normalized hyperspectral data. The first method used a spectral distribution exploration method. The second method used a supervised classification method, more specifically, a support vector machine (SVM) algorithm. The spectral distribution exploration method did not detect any different spectral signature for itchy region. On the other hand, the SVM classifier detected the itchy region with the surrounding non-itchy region. These results demonstrated the feasibility of using hyperspectral imaging combined with classification algorithms for detecting itchy skin region.
Proc. SPIE. 10140, Medical Imaging 2017: Digital Pathology
KEYWORDS: Optical microscopes, Light sources, Tissues, Pathology, Skin, Imaging devices, Multispectral imaging, Color difference, Transmittance, Kidney, Human vision and color perception, Colon, Color reproduction
The color reproducibility of two whole-slide imaging (WSI) devices was evaluated with biological tissue slides.
Three tissue slides (human colon, skin, and kidney) were used to test a modern and a legacy WSI devices. The
color truth of the tissue slides was obtained using a multispectral imaging system. The output WSI images were
compared with the color truth to calculate the color difference for each pixel. A psychophysical experiment was
also conducted to measure the perceptual color reproducibility (PCR) of the same slides with four subjects. The
experiment results show that the mean color differences of the modern, legacy, and monochrome WSI devices are
10.94±4.19, 22.35±8.99, and 42.74±2.96 ▵E<sub>00</sub>, while their mean PCRs are 70.35±7.64%, 23.06±14.68%, and
Non-invasive mechanical property estimation of an embedded object (tumor) can be used in medicine for characterization between malignant and benign lesions. We developed a tactile imaging sensor which is capable of detecting mechanical properties of inclusions. Studies show that stiffness of tumor is a key physiological discerning parameter for malignancy. As our sensor compresses the tumor from the surface, the sensing probe deforms, and the light scatters. This forms the tactile image. Using the features of the image, we can estimate the mechanical properties such as size, depth, and elasticity of the embedded object. To test the performance of the method, a phantom study was performed. Silicone rubber balls were used as embedded objects inside the tissue mimicking substrate made of Polydimethylsiloxane. The average relative errors for size, depth, and elasticity were found to be 67.5%, 48.2%, and 69.1%, respectively. To test the feasibility of the sensor in estimating the elasticity of tumor, a pilot clinical study was performed on twenty breast cancer patients. The estimated elasticity was correlated with the biopsy results. Preliminary results show that the sensitivity of 67% and the specificity of 91.7% for elasticity. Results from the clinical study suggest that the tactile imaging sensor may be used as a tumor malignancy characterization tool.