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Laser speckle imaging is one of the powerful non-invasive imaging techniques to monitor and assess microcirculation parameters. Qualitative analysis of perfusion parameters has been carried out in the recent past. But the quantitative estimation of tissue perfusion parameters like flow velocity and scatterer concentration simultaneously from laser speckle images remains challenging. The introduction of machine learning methods into laser speckle image analysis can help meet these challenges to a great extent. This paper presents an approach for the simultaneous extraction of perfusion parameters, using multi-target regression techniques applied to the extracted features from acquired laser speckle images after Eigen-decomposition filtering. The multi-target regression trees are identified as an effective tool for the simultaneous extraction of flow velocity and scatterer concentration with adequate mean absolute percentage error. Besides the achieved speed and computational efficiency, our work demonstrates the viability of this approach in quantifying perfusion parameters simultaneously. Due to its simple, non-invasive, and cost-effective nature, the proposed technique could be used in the real-time assessment of tissue health.

Optical biopsy is critical in examining tissue specimens for aiding diagnostics. Here, we correlate image entropy, spatial correlation, and Mueller matrix derived orientation parameter to study the organization of collagen in unstained thin scleroderma samples.

The Monte-Carlo polarized light tissue modeling significantly impacts the understanding of biological tissue's geometrical characteristics. We adopt this simulation to differentiate the spherical and cylindrical scatterers present in the medium.

Multilayer silicone phantoms mimicking a resected breast tissue with embedded tumor at varying depths are fabricated and used in autofluorescence imaging applications.

Dermal thickness variation is observed with laser illuminated skin-mimicking phantoms. From the acquired laser speckle images, the contrast ratio and percentage change in intensity autocorrelation of static and dynamic components are estimated.

Spatially resolved diffuse reflectance spectroscopy (SRDRS) is a non-invasive optical technique that helps in clinical diagnosis of various tissue microcirculation and skin pigmentation disorders based on collected backscattered light from multi-layered tissue. The extraction of the optical properties from the reflectance spectrum using analytical solutions is laborious. Model-based light tissue interaction studies help in quantifying the optical properties. This work presents the use of finite element models of light tissue interaction for this purpose. A bilayer model mimicking human skin was considered and the diffused reflectance spectra at multiple detector points were generated using finite element modelling for varying melanin concentration, epidermal thickness, blood volume fraction, oxygen saturation and scattering components. The reflectance value based on varying optical parameters from multiple detection points lead to the generation of a look-up table (LUT), which is further used for finding the tissue parameters that contribute to the spatially resolved reflectance values. The tissue parameters estimated after inverse modelling showed a high degree of agreement with the expected tissue parameters for a test dataset different from the training dataset.

Polarimetric imaging and image analysis have gained increased interest in soft tissue analysis at the cellular level. However, polarimetric imaging has widely been tested on thin tissue sections to provide reliable information correlated with histopathological findings. Polarimetric bulk tissue analysis always offered an overall assessment of various tissue optical properties for diagnosis. In this study, the histopathological correlation of bulk tissue polarimetry images for soft tissues is discussed. The first-hand information on the use of bulk tissue Mueller polarimetry and image analysis as an alternative to tissue histopathology is presented for surgically extracted colon and breast tissues.

The Laser-Induced Breakdown Spectroscopy (LIBS) technique has been utilized in several studies for the identification of pathogenic bacterial strains based on their characteristic spectral fingerprint. Currently used LIBS techniques for discrimination of bacterial strains belonging to the same species require sophisticated methodology and expensive instrumentation. In this study, we present the strategies adopted to achieve this goal using a low-cost LIBS methodology. Time-resolved LIBS experiments were carried out using a nanosecond pulsed Nd: YAG laser (1064 nm, 6 ns, 10 Hz) for ablating the bacterial samples, and the resulting emission spectra were recorded using a portable and non-intensified Charge Coupled Device (CCD) detector. The bacterial strains used for this study were two clinical isolates of Escherichia coli (E. coli) - a pathogen causing severe infections in humans. The individual bacterial strains were cultured using a standard optimized protocol, and their respective chemical fingerprints were captured using the LIBS technique. We also investigate the efficacy of standardizing the growth environment and its role in modulating the chemical composition of the bacterial strains. A bacterial growth study was performed to assess the influence of regulating growth environment (concentration of sodium and potassium in the nutrient media), on the growth phases of the two bacterial strains. The spectral lines corresponding to sodium (589.5 nm), and potassium (766.5 nm, 769.9 nm) were found to be significant among the characteristic LIBS emission of the bacterial strains. The sodium to potassium ratio (Na/K), calculated from the elemental line intensities in the LIBS spectrum of bacteria, was found to be a highly significant feature for discrimination of bacterial strains with a classification accuracy >90%.

The capability of Mueller polarimetry in enhancing contrast finds applications in biomedical diagnosis. In this study, we explore this technique in evaluating bulk specimens and unstained sections of colon tissue for improving diagnostic efficiency.

The thesis contains the detailed research work carried out on various theoretical aspects regarding the interaction between the proposed endo-speckle fluoroscope and the curved colon surface during different modes of diagnosis. DOCTOR OF PHILOSOPHY (MPE)

Mueller polarimetry imaging is gaining acceptance in diagnostic imaging as a potential tool for identifying tissue pathology by the integrated analysis of tissue response to polarized light. The Mueller matrix derived parameters have been related to the structural changes occurring in the tissues during pathogenesis of a disease. An in-house developed Mueller polarimetric imaging system has been explored in this study to differentiate between the normal and sclerodermic regions of unstained skin biopsy samples with varying collagen densities in the dermis. The optical response of excessive collagen deposition in scleroderma gets reflected in the depolarization, diattenuation, and retardance parameters obtained through polar decomposition and singular value analysis. In this study, higher depolarization, retardance and diattenuation values were observed in the scleroderma region as compared to their normal counterpart, suggesting an increased level of collagen deposition, which was further correlated with the histopathological findings. The developed system aims at its deployment in pathology labs to provide stain-free examination procedures in the long run.

Aneurysmal subarachnoid hemorrhage (aSAH) is a severe medical condition associated with a significant cause of mortality throughout the world. Cisterna magna injection model is accepted widely to mimic clinical aSAH and is performed on small animal models to study aSAH during neurosurgery. Coherent light scattered from the surface of the rat brain is used to infer information about the variations in blood flow during this condition. We obtained speckle images from the exposed cortex during the entire experiment using an external tissue imaging system. Contrast and fractal analyses are carried out for the recorded speckle pattern time series. Correlation analysis based on Hurst exponent for these images is found to be a more sensitive tool in studying aSAH as compared to routinely used laser speckle contrast analysis for assessing the changes in blood flow velocity. Additionally, our studies provide improved blood flow detection sensitivity with image Hurst exponent in combination with computed fractal dimension, during an event of aSAH.

Identification of microorganisms using Laser Induced Breakdown Spectroscopy (LIBS) is currently an experimental technique in the early development stage. The highly expensive and complex instrumentation involved in this technique is a severe limitation to its translation into a commercial diagnostic device. In this study, we have investigated the efficacy of using low cost, non-intensified and portable CCD spectrometer systems for identification and discrimination of microorganisms, such as species of gram-positive (Staphylococcus aureus) and gram-negative bacteria (Escherichia coli and Klebsiella pneumoniae) using their spectral fingerprint. Comparison of the resulting emission spectra from these detectors, on ablation using nanosecond pulsed Nd: YAG (1064 nm) laser source has been carried out. We have established the efficiency of classification for discrimination between gram-positive and gram-negative bacteria and also between 2 different species of gram negative bacteria. We also present the results of ablation of the microbial smears on several organic as well as inorganic substrates and their relative efficiency in identification of the microbial species. The results indicate the feasibility of porting this technique onto a PDMS (Polydimethylsiloxane) or paper based microfluidic point of care device which can eliminate the complex sample preparation steps required currently. This study has huge potential for the development of a low-cost commercial LIBS based microfluidic device for detection of microorganisms.

Assessment of skin tissue perfusion is vital for understanding the normal and the pathologic physiology of human body. Diffuse optical methods provide numerous pathways for assessing various static and dynamic perfusion markers such as variations in bulk tissue optical properties, depth dimensions of microvascular bed, rate and volume of blood flow and so on. There have been numerous studies on these aspects ending up with qualitative assessments on various parameters, where separate approaches are explored for individual parametric evaluation. With the introduction of precise optical tissue phantom models, integration of different static and dynamic perfusion markers are possible to facilitate quantitative assessment of such a perfusion matrix. In this work, we present the fabrication of a perfused tissue physical model that mimic skin tissue and subsequent estimation of perfusion matrix including optical properties, flow, and depth of the microvascular bed. Different layers of skin are spin coated onto micron-sized embedded channels, and the model was subjected to optical measurements, inducing different flow levels using a syringe pump. The parameters have been estimated using spatially resolved diffuse correlation optical spectral measurements, using a handheld fiber optic probe with a precise source to target distance sensing mechanism and associated signal processing algorithms. This work is aimed to provide a methodology for quantitative assessment of various perfusion parameters using a versatile physical model that provides flexibility in varying involved parameters accurately. The work performed here, after standardization is expected to have potential in developing non-invasive quantitative optical skin biopsy tools to augment the current histopathological studies.

Optical techniques such as fluorescence and diffuse reflectance spectroscopy are proven to have the potential to provide tissue discrimination during the development of malignancies and hence treated as potential tools for noninvasive optical biopsy in clinical diagnostics. Quantitative optical biopsy is challenging and hence the majority of the existing strategies are based on a qualitative assessment of the concerned tissue. Light–tissue interaction models as well as precise optical phantoms can greatly help in the former and here we present a pilot study to assess the optical properties of a multilayer tissue-specific optical phantom with the help of a database generated using multilayer-Monte Carlo (MCML) models. A set of optical models mimicking the properties of actual and diseased conditions of tissues associated with nonmelanoma skin cancer (NMSC) were devised and MCML simulations of fluorescence and diffuse reflectance were performed on these models to generate the spectral signature of identified biomarkers of NMSC such as hemoglobin, flavin adenine dinucleotide, and collagen. A model library was generated and with the extracted features from modeled spectra, classification of normal and NMSC conditions were tested using the [Formula: see text]-nearest neighbor (KNN) classifier. Using an in-house assembled scan-based automated bimodal spectral imaging system with reflectance and fluorescence modalities of operation, a layered, thin, tissue equivalent phantom, fabricated with controlled optical properties mimicking normal and NMSC conditions were tested. The spectral signatures corresponding to the NMSC biomarkers were acquired from this phantom and extracted features from the spectra were tested using the KNN classifier and classification accuracy of 100% was achieved. For further quantitative analysis, the experimental and simulated spectra were compared with respect to the light intensity at the emission peak or absorption dips, spectral line width, and average intensity over a range of wavelength of interest and observed to be analogous within specified and systematic error limits. This methodology is expected to give a better quantitative approach for estimation of tissue properties by correlating the experimental and simulated data.

Dilutions of Intralipid 20% are widely used as optical phantoms for mimicking scattering properties of turbid media such as tissues. One of the frequently used methodologies for quantifying the scattering coefficient and anisotropy of Intralipid 20% is the use of single-particle Mie scattering theory, which in fact is not valid for nontenuous media. Hence, two methodologies consisting of analytical wave theory and effective medium theory, incorporating particle size distribution and concentration-dependent correlated scattering phenomena, are used to estimate the effective scattering coefficient and anisotropy of Intralipid 20% dilutions (1%-100% v/v) from 380 to 1000 nm.

Assessment of mechanical properties of soft matter is a challenging task in a purely noninvasive and noncontact environment. As tissue mechanical properties play a vital role in determining tissue health status, such noninvasive methods offer great potential in framing large-scale medical screening strategies. The digital speckle pattern interferometry (DSPI)-based image capture and analysis system described here is capable of extracting the deformation information from a single acquired fringe pattern. Such a method of analysis would be required in the case of the highly dynamic nature of speckle patterns derived from soft tissues while applying mechanical compression. Soft phantoms mimicking breast tissue optical and mechanical properties were fabricated and tested in the DSPI out of plane configuration set up. Hilbert transform (HT)-based image analysis algorithm was developed to extract the phase and corresponding deformation of the sample from a single acquired fringe pattern. The experimental fringe contours were found to correlate with numerically simulated deformation patterns of the sample using Abaqus finite element analysis software. The extracted deformation from the experimental fringe pattern using the HT-based algorithm is compared with the deformation value obtained using numerical simulation under similar conditions of loading and the results are found to correlate with an average %error of 10. The proposed method is applied on breast phantoms fabricated with included subsurface anomaly mimicking cancerous tissue and the results are analyzed.

Optical techniques such as fluorescence and diffuse reflectance spectroscopy are proven to have the potential to provide tissue discrimination during the development of malignancies and hence treated as potential tools for noninvasive optical biopsy in clinical diagnostics. Quantitative optical biopsy is challenging and hence the majority of the existing strategies are based on a qualitative assessment of the concerned tissue. Light-tissue interaction models as well as precise optical phantoms can greatly help in the former and here we present a pilot study to assess the optical properties of a multilayer tissue-specific optical phantom with the help of a database generated using multilayer-Monte Carlo (MCML) models. A set of optical models mimicking the properties of actual and diseased conditions of tissues associated with nonmelanoma skin cancer (NMSC) were devised and MCML simulations of fluorescence and diffuse reflectance were performed on these models to generate the spectral signature of identified biomarkers of NMSC such as hemoglobin, flavin adenine dinucleotide, and collagen. A model library was generated and with the extracted features from modeled spectra, classification of normal and NMSC conditions were tested using the [Formula: see text]-nearest neighbor (KNN) classifier. Using an in-house assembled scan-based automated bimodal spectral imaging system with reflectance and fluorescence modalities of operation, a layered, thin, tissue equivalent phantom, fabricated with controlled optical properties mimicking normal and NMSC conditions were tested. The spectral signatures corresponding to the NMSC biomarkers were acquired from this phantom and extracted features from the spectra were tested using the KNN classifier and classification accuracy of 100% was achieved. For further quantitative analysis, the experimental and simulated spectra were compared with respect to the light intensity at the emission peak or absorption dips, spectral line width, and average intensity over a range of wavelength of interest and observed to be analogous within specified and systematic error limits. This methodology is expected to give a better quantitative approach for estimation of tissue properties by correlating the experimental and simulated data.

Scattering property of Intralipid is widely used for calibration and simulation of turbid media, especially biological tissues, in optical spectroscopic studies. The desired phantom turbidity level matching that of target tissue scattering properties is vital in the right preparation of phantoms mimicking the tissue. A simplified two fiber oblique illumination-collection geometry setup is used along with iterative inverse Monte Carlo simulations on the diffuse reflectance obtained experimentally for estimating the reduced scattering coefficient (μś) of Intralipid-20% for wavelengths ranging from 500 nm to 880 nm. Basic Monte Carlo for Multi Layered media (MCML) code is modified to incorporate the two fiber inverse model of diffuse reflectance with oblique broadband illumination and perpendicular collection of diffusively reflected light from the sample. Wavelength dependent true phase function of Intralipid is incorporated in the model and a semi-empirical concentration scaling methodology is used to obtain volume concentration dependence on the μś. In the inverse modelling, the modified Twersky equation for correlated scattering has been used to obtain the μś profile of Intralipid-20% for its volume concentration ranging from 16% to 100%. The results are shown to be in good agreement with the optical characterization studies of Intralipid-20% involving bulkier instrumentation for the wavelength range under consideration. The study presented in this paper gives an insight for an in vivo fiber based methodology for quantifying the variation of optical scattering during tissue malignancy.

The superluminescent diode based differential absorption frequency domain low-coherence interferometry (FD-DALCI) technique is proposed and demonstrated for sensing physiological concentrations of glucose (0-250 mg/dl) in oral mucosa simulating phantoms (intralipid of concentrations 0.25-0.50%) with wavelengths at 1589 and 1310 nm. The proposed technique allows simultaneous measurements of refractive index based spectral shift and estimation of physiological concentration of glucose in intralipid with scattering characteristics using the differential absorption approach. The sensitivity of the glucose concentration obtained by spectral shift measurement was ≈0.016 nm/(mg/dl), irrespective of the intralipid concentration. The resolution of the glucose level was estimated to be ≈15 mg/dl in 0.25% intralipid and ≈19 mg/dl in 0.5% intralipid using the FD-DALCI technique.

Measurement and analysis of microcirculation is vital in assessing local and systemic tissue health. Changes in microvascular perfusion if detected can provide information on the development of various related diseases. Laser Doppler blood flowmetry (LDF) provides a non-invasive real-time measurement of cutaneous blood perfusion. LDF signals possess fractal nature that represents the correlation in the successive signal elements. Changes in the correlation of flow and its associated parameters could be used as a tool in differentiating the ailments at different stages or assessing the treatment effectiveness of a particular ailment. Spectral domain analysis of LDF signals reveals five characteristic frequency peaks corresponding to local and central regulatory mechanisms of the human body, namely metabolic, neurogenic, myogenic, respiration, and heart rate. This paper investigates the changes in the fractal nature and constituent frequency bands of laser Doppler signals in diabetic and healthy control subjects acquired from the glabrous skin of the foot so as to provide an assessment of microcirculatory dynamics. As a pilot study, it was attempted on a set of healthy control and diabetic volunteers, and the obtained results indicate that fractal nature of LDF signals is less in diabetic subjects compared to the healthy control. The wavelet analysis carried out on the set of signals reveals the dynamics of blood flow which may have led to the difference in correlation results. � 2015, International Federation for Medical and Biological Engineering.

Laser based techniques offer non invasive means of imaging and optical signal acquisition in the biomedical field. Laser Doppler flowmetry and laser speckle imaging are important laser based methods in current research and have been explored for the analysis of blood flow. Doppler flow meters as well as laser speckle imagers use tissue backscattered light to non-invasively assess the blood flow rate. While because of large spatial variability and the temporal heterogeneity in tissue microvasculature, the measured blood flow rate is expressed in relative units in laser Doppler, laser speckle methods offers a whole field imaging resulting in absolute measurements of flow velocity. Measurement of and analysis of blood flow is vital in evaluating normal as well as differently diseased conditions of the human body. Several parameters related to flow along with flow velocity are important in characterizing tissues based on blood flow. Complexity of the flow is one of such important parameters which could be explored by looking at the fractality of the acquired Doppler signals / speckle images. In this paper, we are comparing the results of blood flow complexities assessed through fractal dimensions of Doppler signals and speckle images acquired from different parts of the body. The method adopted is expected to serve as a helping tool in characterizing normal and malignant tissues with associated variation in blood flow complexities based on the values of obtained fractal dimensions in such cases.