• Dielectric and Double Debye Parameters of Artificial Normal Skin and Melanoma

      Yang, Bin; Zhang, Rui; Yang, Ke; AbuAli, Najah A.; Hayajneh, Mohammad; Philpott, Mike; Abbasi, Qammer H.; Alomainy, Akram; University of Chester (Springer, 2019-05-16)
      The aim of this study is to characterise the artificial normal skin and melanoma by testing samples with different fibroblast and metastatic melanoma cell densities using terahertz (THz) time-domain spectroscopy (TDS) attenuated total reflection (ATR) technique. Results show that melanoma samples have higher refractive index and absorption coefficient than artificial normal skin with the same fibroblast density in the frequency range between 0.4 and 1.6 THz, and this contrast increases with frequency. It is primarily because that the melanoma samples have higher water content than artificial normal skin, and the main reason to melanoma containing more water is that tumour cells degrade the contraction of the collagen lattice. In addition, complex refractive index and permittivity of the melanoma samples have larger variations than that of normal skin samples. For example, the refractive index of artificial normal skin at 0.5 THz increases 4.3% while that of melanoma samples increases 8.7% when the cell density rises from 0.1 to 1 M/ml. It indicates that cellular response of fibroblast and melanoma cells to THz radiation is significantly different. Furthermore, the extracted double Debye (DD) model parameters demonstrate that the static permittivity at low frequency and slow relaxation time can be reliable classifiers to differentiate melanoma from healthy skin regardless of the cell density. This study helps understand the complex response of skin tissues to THz radiation and the origin of the contrast between normal skin and cancerous tissues.
    • Visual-Inertial 2D Feature Tracking based on an Affine Photometric Model

      Aufderheide, Dominik; Edwards, Gerard; Krybus, Werner; South Westphalia University of Applied Sciences, University of Chester, South Westphalia University of Applied Sciences (Springer, 2015-04-08)
      The robust tracking of point features throughout an image sequence is one fundamental stage in many different computer vision algorithms (e.g. visual modelling, object tracking, etc.). In most cases, this tracking is realised by means of a feature detection step and then a subsequent re-identification of the same feature point, based on some variant of a template matching algorithm. Without any auxiliary knowledge about the movement of the camera, actual tracking techniques are only robust for relatively moderate frame-to-frame feature displacements. This paper presents a framework for a visual-inertial feature tracking scheme, where images and measurements of an inertial measurement unit (IMU) are fused in order to allow a wider range of camera movements. The inertial measurements are used to estimate the visual appearance of a feature’s local neighbourhood based on a affine photometric warping model.