By using our dual-modality system enabling simultaneous real-time ultrasound (US) and photoacoustic (PA) imaging of human peripheral joints, we explored the potential contribution of PA imaging modality to rheumatology clinic. By performing PA imaging at a single laser wavelength, the spatially distributed hemoglobin content reflecting the hyperemia in synovial tissue in metacarpophalangeal (MCP) joints of 16 patients were imaged, and compared to the results from 16 healthy controls. In addition, by performing PA imaging at two laser wavelengths, the spatially distributed hemoglobin oxygenation reflecting the hypoxia in inflammatory joints of 10 patients were imaged, and compared to the results from 10 healthy controls. The statistical analyses of the PA imaging results demonstrated significant differences (p < 0.001) in quantified hemoglobin content and oxygenation between the unequivocally arthritic joints and the normal joints. Increased hyperemia and increased hypoxia, two important physiological biomarkers of synovitis reflecting the increased metabolic demand and the relatively inadequate oxygen delivery in affected synovium, can both be objectively and non-invasively evaluated by PA imaging. The proposed dual-modality system has the potential of providing additional diagnostic information over the traditional US imaging approaches and introducing novel imaging biomarkers for diagnosis and treatment evaluation of inflammatory arthritis.

Breast cancer is one of the leading causes for high mortality rates among young women, in the developing countries. Currently mammography is used as the gold standard for screening breast cancer. Due to its inherent disadvantages, alternative techniques are being considered for this purpose. Breast thermography is one such imaging modality, which represents the temperature variations of breast in the form of intensity variations on an image. In the last decade, several studies have been made to evaluate the potential of breast thermograms in detecting abnormal breast conditions, from an image processing view point. This paper proposes a curvelet transform based feature extraction method for automatic detection of abnormality in breast thermograms. Statistical and texture features are extracted from thermograms in the curvelet domain, to feed a support vector machine for automatic classification. The classifier detects abnormal thermograms with an accuracy of 90.91 %. The results of the study indicate that texture features have better potential to detect abnormality in breast thermograms, when extracted in the multiresolution curvelet domain.

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Thermography is a non-invasive imaging modality that represents surface temperature variations of the skin in the form of images called thermograms. The surface temperature around the area of cancerous cells is slightly higher than normal tissues and this area is seen as hot spots on thermograms. In normal breast thermograms, symmetric heat patterns are observed in both breasts, but in the case of unilateral abnormality, asymmetry is observed. As the intensity variations in thermograms represent surface temperature changes, texture features that would enhance thermal asymmetry, between right and left breasts, have been studied. The texture features are extracted from the breast region and fed to a back propagation neural network for automatic detection of abnormal breast thermograms. The classifier is able to classify abnormal and normal thermograms with an accuracy of 85.19%. From the results of the study, it is inferred that thermography has the potential to detect breast cancer and can be used as an adjunct tool to mammography.

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