Transform-based spatial analyses of medical Infrared (IR) images are found to be useful to extract local information, which can be used to identify the abnormalities associated with in region of interest. In this work, human hand infrared images are analyzed by extracting local spatial features using wavelet transform method. The images for this study were acquired using uncooled micro bolometer with focal plane array technology based medical IR camera with dedicated software having high array resolution and spectral response under controlled protocol. The acquired images were decomposed into Intrinsic Mode Functions (IMFs) using bidimensional empirical mode decomposition. Extrema points were detected using eight connected neighbor window method and interpolated using thin plate spline interpolation technique to generate IMFs. The edge information were extracted from local phase of the first IMF. Edges were detected using phase congruency measure by applying Gabor function based wavelet transform. The results showed that it was possible to detect edges from only the first IMF without being influenced by other IMFs. It was further observed that the edge intermittence that arises due to noise component was reduced by treating images with local phase distributions. Hence, it appears that the edge information extraction could enhance the diagnostic relevance of thermal image analysis. © 2013.