First, a skin probability map is obtained from the input image using a global model.
Instead of exploiting a face detector to acquire the skin sample, we analyze the skin probability map P S obtained from the input color image using a global skin color model.
With this type of artefact, it is impossible to correctly classify the image using a global threshold even after NAD filtering, no matter how sophisticated the selection algorithm may be.
After the OCT image was acquired, it was registered to the corresponding CCD image manually, using a global affine transformation allowing only translation, rotation, and isotropic scaling so that the structure of the measurements stayed unchanged.
Reconstructed three-dimensional images were segmented using a global threshold determined by an iterative technique [ 12].
Each image was corrected for physiological fluctuations (cardiac, respiration) using a global navigator echo correction [42].
The images were binarized to separate the specimen from the background, using a global thresholding procedure.
The images were then binarized to separate the object from the background using a global thresholding procedure.
The image analysis is a subjective assessment and the scoring of subcategories has the potential for crossover when using a global score.
We use a four phase's algorithm to evaluate our method; first we decompose the image using one of the tree methods (BEEMD, LSEEMD, and PHEEMD), second reconstruct the image based on (1) using only the firsts IMFs, third perform a standard deviation filtering using a 3 × 3 window and finally make a global image binarization using Otsu's [17] method.
