Abstract

Sherin M. Youssef
contourlet-based feature extraction for computer aided diagnosis of medical patterns
the paper introduces an integrated model for monitoringdiagnosis of medical patterns. the designed architecture combines contourlet transformsupervised neuro-based classifier for tumor classification of liverbrain tissues of medical images. a contourlet based cad model is proposed to adopt tumor diagnosis for abnormality detection in computed tomography (ct)magnetic resonance (mri) medical images by exploiting correlative information of suspicious lesions of brainliver sections. several enhancement schemes have been introduced for image fusion, noise reduction, feature extractionclassification. feature extraction is adopted for inter-projective feature matching analysis. for each identified region of interest (roi), distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix (sgld)gray level difference statistics matrix for texture description. the simulation results show the superiority of the proposed model for both ctmri images from both the visual qualitythe peak signal to noise ratio (psnr) points of view. the experimental results demonstrated that our proposed scheme can identify tumor regionshelp radiologists as a second reader in some medical images. performance comparison has been conducted between the final developed cad systemother previously developed cad systems.