Quantification of fiber orientation is the key to characterizing the tissue mechanical properties and diagnosing diseases. A center line-based algorithm is presented for estimating the orientation distribution that first skeletonizes a binary image of fibers, followed by orientation estimation using a weight vector summation algorithm along the center line of image. Then we use the orientation at the skeleton to approximate the orientation of each pixel between the boundary and skeleton. The algorithm is applied for characterizing collagen fibers of mouse skins in second harmonic generation (SHG) image, and the circle standard deviation of orientation could be a biomarker to differentiate the naturally aging skins.