An efficient automated biospeckle indexing strategy using morphological and geo-statistical descriptors

Abstract

Biospeckle is caused by statistical interference of coherent beam reflected from a surface having temporal variation due to physiological or biochemical activity. For quantitative evaluation of underlying dynamic speckle activity, different point based and full-field indexing based techniques were proposed in the literature. However, most of the existing techniques involve manual region of interest (ROI) selection, and possess considerable variation of index value with different experimental and analysis parameters (viz. number of frames, degree of correlation, specimen heterogeneity, and others). To circumvent these drawbacks, in this work, we proposed an efficient automated biospeckle indexing technique by combining morphological and geo-statistical operators. Performance of the proposed strategy was analyzed and compared in the controlled environment using different modifications of rotating diffuser based simulation model. Robustness of the proposed strategy was also validated experimentally using different bio-specimens (human finger, seed, carrot and gum arabica). Obtained results demonstrated that the proposed technique has high accuracy for all assessed conditions. Multiple object detection capability of morphological operators was also integrated in the proposed technique to assess biospeckle signature of multiple specimens captured in a single stack of frames. Simultaneous dynamicity assessment of multiple objects from a single stack reduced both computational and experimental overheads considerably. The proposed strategy is useful in biospeckle based quality control and automation.