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.
Accurate and reliable decision making in cancer prognosis can help in the planning of appropriate surgery and therapy and, in general, optimize patient management through the different stages of the disease. In this paper, we present a novel fractal geometry algorithm as a potential method for classifying colorectal histopathological images. 102 microscopic samples of colon tissue were examined in order to identify abnormalities using a morphogical feature approach based on segmenting the image into different classes, derived from fractal dimension. The obtained mean fractal dimension (FD) for normal object tissue was 1.797+/- 0.0381 (n = 44) compared with 1.866+/-0.0262 for malignant samples (n = 58). In brief, this study was able to demonstrate the value of fractal dimension based on morphological approach in the analysis of microscopic colon cancer images. Although, the obtained results are strongly significant in the separation between normal and malignant colorectal images, further analyses are essential to incorporate this methodology into routine clinical practice by supporting pathologist decision.