Automated medical image diagnosis using quantitative measurements is extremely helpful for cancer prognosis to reach a high degree of accuracy and thus make reliable decisions. In this paper, six morphological features based on texture analysis were studied in order to categorize normal and cancer colon mucosa. They were derived after a series of pre-processing steps to generate a set of different shape measurements. Based on the shape and the size, six features known as Euler Number, Equivalent Diamater, Solidity, Extent, Elongation, and Shape Factor AR were extracted. Mathematical morphology is used firstly to remove background noise from segmented images and then to obtain different morphological measures to describe shape, size, and texture of colon glands. The automated system proposed is tested to classifying 102 microscopic samples of colorectal tissues, which consist of 44 normal color mucosa and 58 cancerous. The results were first statistically evaluated, using one-way ANOVA method in order to examine the significance of each feature extracted. Then significant features are selected in order to classify the dataset into two categories. Finally, using two discrimination methods; linear method and k-means clustering, important classification factors were estimated. In brief, this study demonstrates that abnormalities in low-level power tissue morphology can be distinguished using quantitative image analysis. This investigation shows the potential of an automated vision system in histopathology. Furthermore, it has the advantage of being objective, and more importantly a valuable diagnostic decision support tool.