A brain tumor detection system using gradient based watershed marked active contours and curvelet transform

Computer aided brain tumor detection is an efficient research area in brain image processing. In this study, a methodology called GWMAC-CT (gradient based watershed marked active contours and curvelet transform) is proposed to detect the brain tumors in magnetic resonance (MR) images. The implemented system is based on skull removing, segmentation of region of interest (ROI), feature extraction, and ROI classification as tumor or nontumor. The proposed GWMAC is a two-stage segmentation method which includes gradient based watershed transform (GWT) and improved active contours. The rough ROIs obtained with GWT are utilized as initial contours for the improved active contours method instead of marking initial contours manually. Curvelet transform-based features of the exact ROI contours are classified via well-known classification methods such as support vector machine (SVM), K-nearest neighbors, random forest tree, and Naive Bayes. Experiments are carried out on a set of brain MR images from BRATS database to demonstrate the effectiveness of the proposed method. The performance evaluators such as accuracy, kappa statistics, false positive rate, precision, F1-measure, and area under ROC curve are calculated as 96.81%, 0.927, 0.046, 0.905, 0.95, and 0.977, respectively with SVM

Minutiae-Based Fingerprint Identification Using Gabor Wavelets and CNN Architecture

Fingerprint identification is still a challenging issue for confident authentication. In this study, we present a methodology that comprises pre-processing, minutiae detection, and Gabor wavelet transform. Both Gabor wavelet and minutiae features, such as ridge bifurcation and ending enhancement, represent the significant information belonging to fingerprint images. Pre-processing algorithm affects minutiae extraction performance. So we use the dilation morphological operation and thinning for the enhancement. Then Gabor wavelet transform is applied to minutiae extracted images to increase the identification performance. The classification problem is solved using a proper convolutional neural network (CNN) with a three layer convolutional model and appropriate filter sizes. Experimental results demonstrate that the classification accuracy is 91.50% and the proposed approach can achieve good results even with poor quality images