Reconstruction of the absorption coefficient of tissue with good contrast is of key importance in functional diffuse optical imaging. A hybrid approach using model-based iterative image reconstruction and a genetic algorithm is proposed to enhance the contrast of the reconstructed image. The proposed method yields an observed contrast of 98.4%, mean square error of 0.638×10−3, and object centroid error of (0.001 to 0.22) mm. Experimental validation of the proposed method has also been provided with tissue-like phantoms which shows a significant improvement in image quality and thus establishes the potential of the method for functional diffuse optical tomography reconstruction with continuous wave setup. A case study of finger joint imaging is illustrated as well to show the prospect of the proposed method in clinical diagnosis. The method can also be applied to the concentration measurement of a region of interest in a turbid medium.
Diffuse optical tomography (DOT), a noninvasive imaging modality, uses near infrared light to illuminate the tissue and
reconstructs the optical parameters of the tissue from the intensity measurements at the surface. Here continuous wave
measurement with improved localization is proposed to make the overall instrument inexpensive. Due to the non-unique
solution of the inverse problem, prior information improves the resolution of the reconstructed image. An artificial neural
network (ANN) based approach is developed to obtain the location of the inclusion. The peak amplitude, 50% and 10%
bandwidth and their corresponding source-detector angles of the difference intensity plot with and without the inclusion
are taken as the input. The offset distance between the source and centre of inclusion, the angle with x-axis, sample and
inclusion radii are the output of the 2 layered error back propagation neural network. Least square optimization with
regularization term is used to minimize the mean squared error for image reconstruction. The optical parameters are
updated using the prior information from the ANN. The parameters present in double the region of detected area only are
updated. The performance of the proposed method has been assessed quantitatively by computing the mean square error,
object centroid error and misclassification ratio. The use of prior improves the convergence and reduces the presence of
ghost or noise. Hence the proposed method shows potential to improve DOT reconstruction.
Direct noninvasive visualization of wound bed with depth information is important to understand the tissue repair. We correlate skin swept-source-optical coherence tomography (OCT) with histopathological and immunohistochemical evaluation on traumatic lower limb wounds under honey dressing to compare and assess the tissue repair features acquired noninvasively and invasively. Analysis of optical biopsy identifies an uppermost brighter band for stratum corneum with region specific thickness (p < 0.0001) and gray-level intensity (p < 0.0001) variation. Below the stratum corneum, variation in optical intensities is remarkable in different regions of the wound bed. Correlation between OCT and microscopic observations are explored especially in respect to progressive growth and maturation of the epithelial and subepithelial components. Characteristic transition of uniform hypolucid band in OCT image for depigmented zone to wavy highly lucid band in the pigmented zone could be directly correlated with the microscopic findings. The transformation of prematured epithelium of depigmented area, with low expression of E-cadherin, to matured epithelium with higher E-cadherin expression in pigmented zone, implicated plausible change in their optical properties as depicted in OCT. This correlated evaluation of multimodal images demonstrates applicability of swept-source-OCT in wound research and importance of integrated approach in validation of new technology.
The goal of this paper is to present our work on the analysis of speech and handwriting biometrics related to meta data, which are based on one side on system hardware specifics (technical meta data) and on the other side to personal attributes (non-technical meta data). System related meta data represent physical characteristics of biometric sensors and are essential for ensuring comparable quality of the biometric raw signals. Previous work in personal related meta data has shown that it is possible to estimate some meta data like script language, dialect, origin, gender and age by statistically analyzing human handwriting and voice data. On one hand, by knowing both kinds of such meta data, it appears to be possible to adapt the recognition or authentication algorithms in order to enhance their recognition accuracy and to analyze the sensor dependency of biometric algorithms with respect to hardware properties such as sampling resolution. On the other hand, interesting aspects are to evaluate, if cultural characteristics (such as native language, or ethnicity) can be derived by statistical or analytical means from voice or handwriting dynamics and to which degree grouping of users by persons with identical or similar meta data may result in better biometric recognition accuracy. All these aspects have been widely neglected by research until today. This article will discuss approaches to model such meta data and strategies for finding features by introducing a new meta data taxonomy, from which we derive those personal and system attributes related to the cultural background, which are employed in our experimental evaluation. Further, we describe the test methodology used for our experimental evaluation in different cultural regions of India and Europe and present first results for sensor hardware related meta data in handwriting biometrics as well as language related meta data in speaker recognition.