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A big data eHealth system would be very helpful in evaluating these changes. We usually analyze images of the wound bed for diagnosing injury. Unfortunately, accurate measurements of wound region changes from images are difficult. Many factors affect the quality of images, such as intensity inhomogeneity and color distortion. To this end, we propose a fast level set model\u2010based method for intensity inhomogeneity correction and a spectral properties\u2010based color correction method to overcome these obstacles. State\u2010of\u2010the\u2010art level set methods can segment objects well. However, such methods are time\u2010consuming and inefficient. In contrast to conventional approaches, the proposed model integrates a new signed energy force function that can detect contours at weak or blurred edges efficiently. It ensures the smoothness of the level set function and reduces the computational complexity of re\u2010initialization. To increase the speed of the algorithm further, we also include an additive operator\u2010splitting algorithm in our fast level set model. In addition, we consider using a camera, lighting, and spectral properties to recover the actual color. Numerical synthetic and real\u2010world images demonstrate the advantages of the proposed method over state\u2010of\u2010the\u2010art methods. Experimental results also show that the proposed model is at least twice as fast as methods used widely. 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