Can spectral-spatial image segmentation be used to discriminate experimental burn wounds?

Hyperspectral imaging (HSI) is a noncontact and noninvasive optical modality emerging the field of medical research. The goal of this study was to determine the ability of HSI and image segmentation to discriminate burn wounds in a preclinical porcine model. A heated brass rod was used to introduce burn wounds of graded severity in a pig model and a sequence of hyperspectral data was recorded up to 8-h postinjury. The hyperspectral images were processed by an unsupervised spectral­spatial segmentation algorithm. Segmentation was validated using results from histology. The proposed algorithm was compared to K-means segmentation and was found superior. The obtained segmentation maps revealed separated zones within the burn sites, indicating a variation in burn severity. The suggested image-processing scheme allowed mapping dynamic changes of spectral properties within the burn wounds over time. The results of this study indicate that unsupervised spectral­spatial segmentation applied on hyperspectral images can discriminate burn injuries of varying severity.

Hyperspectral imaging for detection of arthritis: feasibility and prospects.

Rheumatoid arthritis (RA) is a disease that frequently leads to joint destruction. It has a high incidence rate worldwide, and the disease significantly reduces patients' quality of life. Detecting and treating inflammatory arthritis before structural damage to the joint has occurred is known to be essential for preventing patient disability and pain. Existing diagnostic technologies are expensive, time consuming, and require trained personnel to collect and interpret data. Optical techniques might be a fast, noninvasive alternative. Hyperspectral imaging (HSI) is a noncontact optical technique which provides both spectral and spatial information in one measurement. In this study, the feasibility of HSI in arthritis diagnostics was explored by numerical simulations and optimal imaging parameters were identified. Hyperspectral reflectance and transmission images of RA and normal human joint models were simulated using the Monte Carlo method. The spectral range was 600 to 1100 nm. Characteristic spatial patterns for RA joints and two spectral windows with transmission were identified. The study demonstrated that transmittance images of human joints could be used as one parameter for discrimination between arthritic and unaffected joints. The presented work shows that HSI is a promising imaging modality for the diagnostics and follow-up monitoring of arthritis in small joints.