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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.

Spinous layer being a sub compartment of surface epithelium of oral mucosa, plays also major role in investigating oral submucous fibrosis (OSF) at its early stage in addition to basal layer. This paper aims to provide a technique that can be used to assist the oral pathologists in grading OSF based on textural information of the spinous layer. The proposed scheme intends to evaluate the textural changes from normal to various grades of OSF. In practice, it comprises the following modules-(a) surface epithelium segmentation, (b) selection of windows on the spinous layer in reference to the basal layer, (c) textural feature extraction and analysis and finally (d) grading. Here the epithelium is segmented using anisotropic diffusion and Otsu's thresholding. Wavelet based multi-resolution technique is applied to extract 12 textural features from spinous layer. From the statistical analysis, it is observed that 6 features are significant in discriminating normal, OSF with and without dysplasia. Finally, support vector machine (SVM) and Bayesian classifiers are trained with 46 normal, 24 OSF without dysplasia and 20 OSF with dysplasia samples for OSF grading. The result shows that the classification accuracies for both the classifiers (Bayesian = 93.3%, SVM = 96.6%) are comparable, there by emphasizing the significance of texture in oral cancer diagnostics.