Alzheimer’s disease (AD) affects the quality of life as it causes; memory loss, difficulty in thinking, learning, and performing familiar tasks. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate and analyze different brain regions for AD identification. This study investigates the effectiveness of using correlated transfer function (CorrTF) as a new biomarker to extract the essential features from rs-fMRI, along with support vector machine (SVM) ordered hierarchically, in order to distinguish between the different AD stages. Additionally, we explored the regions, showing significant changes based on the CorrTF extracted features’ strength among different AD stages. First, the process was initialized by applying the preprocessing on rs-fMRI data samples to reduce noise and retain the essential information. Then, the automated anatomical labeling (AAL) atlas was employed to divide the brain into 116 regions, where the intensity time series was calculated, and the CorrTF features were extracted for each region. The proposed framework employed the SVM classifier in two different methodologies, hierarchical and flat multi-classification schemes, to differentiate between the different AD stages for early detection purposes. The ADNI rs-fMRI dataset, employed in this study, consists of 167, 102, 129, and 114 normal, early, late mild cognitive impairment (MCI), and AD subjects, respectively. The proposed schemes achieved an average accuracy of 98.2% and 95.5% for hierarchical and flat multi-classification tasks, respectively, calculated using ten folds cross-validation. Therefore, CorrTF is considered a promising biomarker for AD early-stage identification. Moreover, the significant changes in the strengths of CorrTF connections among the different AD stages can help us identify and explore the affected brain regions and their latent associations during the progression of AD.

I S S N 2277-3061 V o l u m e 1 5 N u m b e r 1 1 I n t e r n a t i o n a l J o u r n a l o f C o m p u t e r s & T e c h n o l o g y 7 2 1 8 | P a g e c o u n c i l f o r I n n o v a t i v e R e s e a r c h A u g u s t 2 0 1 6 w w w . c i r w o r l d . ABSTRACTCardiovascular diseases (CVDs) cause 31% of the death rate globally. Automatic accurate segmentation is needed for CVDs early detection. In this paper, we study the effect of the registration and initialization of the level set segmentation on the performance of extracting the heart ventricles for the short axis cardiac perfusion MRI images, as a result, we propose a modified workflow to automatically segment the ventricles by mitigating the levelset initial contour extraction in order to improve the segmentation results accuracy. In the registration experiments, the translational transformation was studied based on both the spatial and frequency domain. The frequency domain based registration is mainly established based on the phase correlation methodology. As for the segmentation experiments, the level set initialization was done through extracting the ventricles' real shape from each slice. Though, the final contour of any frame will be used as the initial contour for the next frame. The second initialization strategy was based on defining the initial contour for each frame using the polar representation of the image. Two short axis view datasets of cardiac magnetic resonance (CMR) perfusion imaging were used in testing the proposed methods. Dice coefficient, sensitivity, specificity and Hausdorff distance have been used to evaluate and validate the segmentation results. The system workflow consists of five main modules: preprocessing, localization, initial contour extraction, registration, and segmentation. The segmentation accuracy for left and right ventricles improved from 72% to 77% and from 70% to 81% using the spatial domain based registration algorithm. The polar-based initialization strategy improves the segmentation accuracy from 77% to 81% and from 81% to 82% for the left and right ventricles respectively.

Lung cancer causes the most number of deaths worldwide in both men and women. Early detection and diagnosis can minimize the disease mortality rate. Commonly, chest computed tomography (CT) scans are used by clinicians to diagnose lung cancer. The lung cancer diagnosis relies on detection of the pulmonary nodules in CT scans. In this paper, we propose computer-aided diagnostic systems that can define and suggest the most important features that can distinguish lung nodule from nonnodule one. The proposed system can be described through the following six steps: (a) Patch Extraction, (b) Image Preprocessing, (c) Feature Extraction, (d) Normalization, (e) Feature Reduction, and (f) Patch Classification. Feature extraction and selection are the most important steps in any disease classification process. A combination of 132 texture features with three shape-based features has been extracted. Then the normalization step has been done using min–max method followed by the feature reduction step based on the wrapper approach. The feature reduction step resulted in selecting a set of eight features for the classification process. The algorithm was developed and tested using 166 patches of CT images. The selected eight features achieve accuracy 96.5% using [Formula: see text]-nearest neighbor classifier. The results were validated using the cross-validation technique, [Formula: see text]-fold method.

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