A Comparison of Tumor Detection Approaches Based on K-means+GA, Watershed Clustering, and Otsu Threshold Method

Abstract

Objective: Tumor detection from medical images using image processing approaches is an essential step in the treatment of patients. In this study, three tumor detection approaches based on segmentation are proposed.

Methods: The first method uses genetic algorithm to optimize cluster centers of K-means clustering. The second method utilizes the Otsu threshold method to detect tumor areas. The third method is based on morphological operators and watershed clustering to detect tumor areas.

Result: The mentioned methods are applied to three images and the accuracy of results is assessed using the confusion matrix. Results show that the methods have an error about 2 to 6 percent in detecting tumor areas, which it indicates a high accurate results. Moreover, accuracy of K-means and Otsu methods in separating tumor and no-tumor areas is higher than that of the watershed method.

Conclusion: The three clustering methods are appropriate to separate tumor and no-tumor areas and their results are robust.