Framework for the Comparison of Classifiers for Medical Image Segmentation with Transform and Moment based features

The paper depicts and elaborates a new framework for the comparison of classifiers for medical image segmentation with transform and moment based features. Medical images modalities such as Ultrasound (US) bladder, Ultrasound (US) phantom, Computerized Tomography (CT) and Magnetic Resonance (MR) images are segmented using different algorithms namely, k-Nearest Neighbor (kNN), Grow and learn (GAL) and Incremental Supervised Neural Networks (ISNN). Segmentation is performed by applying feature extraction methods such as 2D Continuous Wavelet Transform (2D-CWT), Moments of gray level histogram (MGH) and a combined version of both 2D-CWT and MGH, called Hybrid features. With different iterations, the analysis results indicate that kNN performs better than GAL, and the performance of GAL is better than that of the ISNN for image segmentation. During analysis a comparison has been drawn between the performance of kNN, GAL and ISNN on the above three feature extraction schemes and also provides the qualitative and quantitative analysis of three classifiers. Results indicate that the performance of 2D-CWT and that of Hybrid features is consistently better than MGH features for all image modalities. The demonstrated frame work or the system is capable to meet the demand for selecting best approach in order to meet the given time constraints and accuracy standards in medical image segmentation.