Spectral Imaging of Near-Surface Oxygen Saturation

A number of non invasive methods have been developed to characterize parameters in near-surface skin tissue; however, the work has usually been concerned with using either spectral or spatial information. This motivated our study in which both spatial and spectral data are used to extract features for characterizing the spatial distribution of near-surface oxygen saturation. This paper addresses combined physical and statistical models to retrieve the ratio of oxy- and deoxy-hemoglobin in tissues from data collected by an imaging spectrometer. To retrieve the oxygen saturation fraction from the data, algorithms from the literature using two or three wavelengths were compared to our new algorithm using the many more wavelengths (25 to 60) available in imaging spectrometer data, and noise reduction achieved through principal component transformations. In addition to the analysis of experimental spectral imagery, an oxygen saturation phantom of size 128x128 pixels was simulated. In the forward process, a reflectance image was constructed from an assumed oxygen saturation map and the absorption coefficients of oxy-hemoglobin, deoxy-hemoglobin, melanin and other chromophores. The reflectance data have 60 bands spanning 400 nm to 990 nm with 10 nm intervals in the spectral dimension. Varying amounts of white Gaussian noise was added to the reflectance data to simulate measurement errors in an actual experiment. In the backward process, an oxygen saturation image was reconstructed by applying the algorithm to study the effect of measurement error on the retrieved saturation fraction. The resultant images were evaluated by their mean squared error

Imaging ductal carcinoma using a hyperspectral imaging system

Hyperspectral Imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues, as well as early and late stages of breast cancer. If the spectral differences in these tissue types can be measured, automated systems can be developed to help the pathologist identify suspect biopsy samples, which will improve sample throughput and assist in making critical treatment decisions. Tissue samples from ten different patients were provided by the WVU Pathology Department. The samples from each patient included both normal and ductal carcinoma tissue, both stained and unstained. These cells were imaged using a snapshot HSI system, and the spectral reflectances were evaluated to see if there was a measurable spectral difference between the various cell types. Analysis of the spectral reflectance values indicated that wavelengths near 550nm show the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. K-Means and Support Vector Machine (SVM) approaches were applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with TNR of 95.8%, and FPR of 4.2%. These results were verified by ground truth marking of the tissue samples by a pathologist. This interdisciplinary work will build a bridge between pathology and hyperspectral optical diagnostic imaging in order to reduce time and workload on the pathologist, which can lead to benefit of lead reducing time, and increasing the accuracy of diagnoses

Quantification of prognostic parameters for assessment of diabetes-related foot ulcers and venous leg ulcers using image processing techniques

Diabetes-related Foot Ulcers (DFUs) and Venous Leg Ulcers (VLUs) are two important types of chronic wounds which do not heal in an orderly fashion and being a major cause of morbidity in extreme cases. Assessment of these ulcers is a growing concern among health care professionals across the globe. Currently, healing of these ulcers is assessed by monitoring the changes in their area over four consecutive weeks. The suggested clinical monitoring parameter is that the ulcers which show more than fifty percent reduction in the area by week four (after the ulcers are reported in the clinics) are predicted to heal within twelve weeks of time. However, this is a subjective measurement, performed manually using a ruler; based on the assumption that the ulcer is purely rectangular. Moreover, the above-mentioned monitoring method fails to work in most of the cases, as healing of these ulcers is a complicated, multifactorial process which cannot be assessed only by a single parameter (i.e., area). This research work has proposed new objective parameters for assessment and prediction of these ulcers by studying the shape of the ulcers, temperature distribution of the ulcer and on the ulcerated foot and area measurement of the ulcers using different techniques. This work has also examined the association of the proposed parameters with patient's clinical information, etiological factors and the healing status of the ulcers. Literature has suggested that there is a change in the irregularity of the ulcers as they heal, thus, playing the role in the healing of the ulcers. Based on this fact, this work hypothesized that the edges of the ulcers can be assessed by quantifying the change in irregularity in them. The widely used technique of measurement of irregularity is Fractal Dimension (FD). However, FD has the limitation of inherent limited resolution of the digitized images, which renders these images as non-fractal, as the self-similarity properties of the images are lost. Thus, this work proposed a new index measure and developed an algorithm for measurement of irregularity and tested it on synthetic images initially. The new index measure called curve irregularity index (Ic) measures the change in the irregularity of the segments of the contours with change in window sizes and does not assume the objects to have self-similar properties. The Ic was then measured and validated on the contours of DFUs and VLUs and the association of irregularity of the contours with etiological factors and the healed status of the ulcers, respectively, was reported. This work has employed the normal DSLR camera and digital planimetry technique to capture the RGB images and to obtain the tracings of the ulcers respectively. This work has shown the significance of contour irregularity of ulcers with the clinical conditions of patients and differentiated between the healed and not-healed ulcers. This research has also employed infrared thermal imaging technique to obtain the temperature distribution of the ulcers. Literature has reported the association of temperature with the risk of ulceration in neuropathic and ischemic conditions of the feet. However, very few works have been done on the study of temperature of the existing ulcers. Hence, this work tested and obtained the association of the mean temperature of the DFUs with the clinical conditions of the participants. This work also hypothesized that the area obtained based on thermal distribution can differentiate between the healed and not-healed ulcers in VLUs. Hence, segmentation of the ulcer region from the thermal images was performed based on an active contour model, previously developed for segmentation of contours in images where edges are not defined by gradient. The obtained results showed that the area thus obtained from the ulcer regions of all five weeks showed association with the healed status of the VLUs and can differentiate between the healed and not-healed ulcers. This work can also be used to predict the healing trajectory of the ulcers. Thus, the overall research work would find application in the clinical set ups to aid in the assessment and prediction of the healing status of DFUs and VLUs and would lead to provide better health-related quality of lives to the patients

Tissue Damage Characterization Using Non-invasive Optical Modalities

The ability to determine the degree of cutaneous and subcutaneous tissue damage is essential for proper wound assessment and a significant factor for determining patient treatment and morbidity. Accurate characterization of tissue damage is critical for a number of medical applications including surgical removal of nonviable tissue, severity assessment of subcutaneous ulcers, and depth assessment of visually open wounds. The main objective of this research was to develop a non-invasive method for identifying the extent of tissue damage underneath intact skin that is not apparent upon visual examination. This work investigated the relationship between tissue optical properties, blood flow, and tissue viability by testing the hypotheses that (a) changes in tissue oxygenation and/or microcirculatory blood flow measurable by Diffuse Near Infrared Spectroscopy (DNIRS) and Diffuse Correlation Spectroscopy (DCS) differ between healthy and damaged tissue and (b) the magnitude of those changes differs for different degrees of tissue damage. This was accomplished by developing and validating a procedure for measuring microcirculatory blood flow and tissue oxygenation dynamics at multiple depths (up to 1 centimeter) using non-invasive DCS and DNIRS technologies. Due to the lack of pressure ulcer animal models that are compatible with our optical systems, a proof of concept was conducted in a porcine burn model prior to conducting clinical trials in order to assess the efficacy of the system in-vivo. A reduction in total hemoglobin was observed for superficial (5%) and deep burns (35%) along with a statistically significant difference between the optical properties of superficial and deep burns (p < 0.05). Burn depth and viable vessel density were estimated via histological samples. 42% of vessels in the dermal layer were viable for superficial burns, compared to 25% for deep burns. The differences detected in optical properties and hemoglobin content by optical measurements correlated with the extent of tissue injury observed in histological stains. After proof of concept in animals, a human study was conducted and optical data was collected from 20 healthy subjects and 8 patients at risk of developing pressure ulcers. Blood flow index (BFI) values from the sacral region of patients were compared with those of healthy volunteers. Prior to loading measurements, baseline BFI values were measured in subjects in lateral position. These values were systematically higher for patients who developed open ulcers than for the other research subjects. While under the loading position, patients who developed a pressure ulcer had a decrease in BFI from baseline values an order of magnitude larger than healthy subjects (p < 0.01) and patients whose redness dissipated (p < 0.01). The hyperemic response, when pressure was released as the patient was moved back to a lateral position, showed a decreasing trend from one session to the next for patients who developed open ulcers. Overall, this work presents a novel non-invasive method of pressure ulcer assessment and provides an improvement over current assessment methods. The obtained results suggest the system may potentially predict whether non-blanchable redness will develop into an advanced pressure ulcer within four weeks from initial observation.Ph.D., Biomedical Engineering -- Drexel University, 201