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The digital image processing has been applied in several areas, especially where it is necessary to use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but it is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this work aims to presents an adaptable segmentation method, that can be applied to different type of images, providing a better segmentation. The proposed method is based on a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold.