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9 March 2011 Analysis of transient thermal images to distinguish melanoma from dysplastic nevi
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Abstract
We have recently developed a dynamic infrared (IR) imaging system that provides accurate measurements of transient thermal response of the skin surface for characterizing lesions. Our hypothesis was that malignant pigmented lesions with increased proliferative potential generate quantifiable amounts of heat and possess an ability to reheat more quickly than the surrounding normal skin, thereby creating a marker of melanoma lesions vs. non-proliferative nevi. In our previous studies, we demonstrated that the visualization and measurement of the transient thermal response of the skin to a cooling excitation can aid the identification of skin lesions of different origin. This capability of distinguishing benign from malignant pigmented lesions is expected to improve the specificity and sensitivity for melanoma as well as other skin cancers, while decreasing the number of unnecessary biopsies. In this work, in order to quantify the transient thermal response with high accuracy, we present a processing framework on multimodal images, which includes a feature point (landmark) detection module, an IR image registration module that uses the resulting landmarks to correct involuntary body/limb motion and an interactive white-light image segmentation module to delineate the contours of the lesions. The proposed method is tested in a pilot patient study in which all the patients possess a pigmented lesion with a clinical indication for biopsy. After scanning, biopsying, and grading the lesions for malignant potential, we observe that the results of our approach match well with the biopsy results.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Muge Pirtini Çetingül, Hasan E. Çetingül, and Cila Herman "Analysis of transient thermal images to distinguish melanoma from dysplastic nevi", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79633N (9 March 2011); https://doi.org/10.1117/12.877858
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