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Optical coherence tomography (OCT) is an imaging technique based on the low coherence interferometry, in which signals are obtained based on the coherent addition of the back reflected light from the sample. Applying computational methods and automated algorithms towards the classification of OCT images allows a further step towards enhancing the clinical applications of OCT. One attempt towards classification could be achieved by statistically analyzing the texture of the noisy granular patterns - speckles that make the OCT images. An attempt has been made to quantify the scattering effects based on the speckle texture patterns the scatterers produce. Statistical inference is drawn from the textural analysis of the features based on the spatial intensity distribution on the agar phantoms with different concentration of Intralipid solutions. This preliminary study conducted on agar-Intralipid solution has showed us that it is possible to differentiate between different types of scatterers based on the speckle texture studies. The texture analysis has also been extended in an attempt to identify the invasion of melanoma cell into tissue engineered skin. However using the same approach of texture analysis, we have not obtained satisfactory results for carrying on with the computer-based identification of the invasion of the melanoma in the tissue engineered skin, the reason for which has to be further studied and investigated upon. Refereed/Peer-reviewed