A flowchart of our analysis procedure is shown in Fig. 1. All the procedures are performed in MATLAB 7.11 (MathWorks, Natick, Massachusetts). The images’ brightness and contrast are adjusted by histogram equalization to standardize the input to the program. Each image’s average sarcomeric separation (SS) and orientation relative to the muscle fiber axis are detected automatically by analyzing the location and orientation of the peaks corresponding the sarcomeres in the Fourier spectrum of the original image. For fibers in extremely bad condition, manually measuring the SS and orientation is also necessary. Because these two parameters are very important for the final scores, the values will also be verified at later steps. The program then generates a set of gray-level, cooccurrence matrices (GLCM), a quantitative measure for the texture features proposed by Haralick et al.21 The dimensions of the matrices are the number of gray levels in the images (i.e., 256 for 8-bit images). To avoid artefacts due to very small intensity fluctuations and to accelerate calculations we bin the gray levels into eight values. For each pixel of the image we then compare its gray level to the gray level of a pixel separated in the fiber axis direction by a distance (the offset). This is repeated for each value of from to (in pixels). One matrix is built for each value of . The elements of the matrices are the relative frequencies with which two pixels separated by , have gray levels and . The offset depends on the image resolution and magnification; for a objective, zoom 2, and images on the SP5, pixel size is 190 nm; assuming a 2-μm SS there are and the length of the array will be 42 pixels. We have generated up to 150 GLCM matrices for images acquired with a magnification or higher. Several textural features, such as homogeneity, contrast, correlation, and entropy, can be extracted from these gray-level, cooccurrence matrices.22 In this study we use the texture correlation, which quantitatively measures joint probability occurrence of the specified pixel pairs, and is defined by Display Formulain which , , , and are the means and standard deviations of and [ and ]. The texture correlation, , ranges from to , where means that two pixels separated along fiber axis by the distance have the same gray level, while means that when one pixel’s brightness reaches maximum, the other pixel’s is minimum. In consequence, a striated muscle image will have a periodic pattern in the texture correlation plot ( versus ), which is shown in the middle column of Fig. 2. The mean peak distance is measured for verifying the sarcomere spacing obtained at the earlier step.