Research Journal of Recent Sciences ________________________________________________ ISSN 2277-2502 Vol. 2(6), 1-10, June (2013) Res.J.Recent Sci. International Science Congress Association 1 Framework for the Comparison of Classifiers for Medical Image Segmentation with Transform and Moment based features Maria Hameed, Muhammad Sharif, Mudassar Raza, Syed Waqas Haider, Muhammad Iqbal Department of Computer Sciences, COMSATS Institute of Information Technology, Wah Cantt., 47040, PAKISTAN Available online at: www.isca.in Received 10 th October 2012, revised 14 th November 2012, accepted 7 th February 2013 Abstract 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. Keywords: kNN, GAL, ISNN, 2D-CWT, MGH Introduction Automatic tissue segmentation of images is helpful for radiologists, as it is used to facilitate doctors during diagnosis. Segmentation of medical images means to classify and identify the structure of interest in medical images. The overall objective is the computer-aided identification of the area of interest to help the doctors and radiologist during diagnosis and treatment of specific disease. Feature extraction is used for extracting sufficient and desired information from the image resulting by different variations from its features. Peculiar features having relevant information are chosen failing which culminates the segmentation process not to be executed correctly/properly 1-5 . For extracting right features, there is need of efficient feature extraction methods. In this paper three transform and moment based segmentation techniques namely, 2D-CWT, MGH and hybrid are analyzed with three different classifiers. In the literature, there are several approaches for image segmentation to be used for different applications, such as edge detection based segmentation 6 , region growing based segmentation method 7 , threshold based segmentation 8 , level set method based segmentation 9 , neural network based segmentation techniques 10 , Watershed algorithm based segmentation 3,5 , graph theory based segmentation 1,11 , clustering based segmentation 12 , active counter model based segmentation 10,13 , Marcove random field model based segmentation 14 , deformable model based segmentation 15 and improved mean shift based segmentation 16 . In the literature there are different transform and texture features extraction based segmentation approaches are found 17-19 . Similarly 2D continuous, discrete wavelet transforms and 2D discrete cosine transform based feature extraction methods for segmentation are represented by Wang et al. and Ghazali et al. 20-21 . The main problem with some of the above methods is that they need too much computational resources and time for segmentation process. Some of them require too many parameters for proper performance yet these fail to meet the desired performance level. The main work in this paper is to find out the best combination of classifiers with feature extraction schemes to achieve efficient segmentation for medical images. Recently, grow and learn (GAL) and incremental supervised neural network (ISNN) are compared under two feature extraction methods (moment of grey level histogram (MGH) and two dimension continuous wavelet transform (2D-CWT)). Neural network and SVM based classifiers 22 are compared to check which classifier has better performance. Similarly, different classifiers 23-24 are compared for checking performance results. In this paper KNN, GAL and ISNN under MGH, 2D-CWT and hybrid are comparatively analyzed to find out best combination of classifier and feature extraction scheme. Methodology In the proposed work kNN, GAL and ISNN are compared with each other as classifiers under MGH, 2D-CWT and hybrid