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This work presents an image reconstruction technique for noise suppression in FLAIR MRI with white matter lesions (WML). The technique utilizes a fuzzy edge estimate to initially localize edge information. Edge and intensity information are coupled through the conditional expectation operator, resulting in a robust and global description of the edge content in the image. As this global measure separates noise and useful edge information, a threshold is used to suppress the irrelevant details and integration is used to reconstruct the “noisefree” image. The threshold is automatically determined based on an objective function that minimizes noise (within class scatter) while maximizing contrast between the WML and brain tissue classes (between class variance). The result is an edge preserving smoothing filter, since the image is reconstructed based on the edge map. The proposed method was compared to the bilateral filter and was found to provide on average a 44.39% increase of noise attenuation in flat regions (smoothness) and a 34.14% increase in edge amplification (enhancement).