Medical image registration plays an important role in many clinical applications, including the detection and diagnosis of diseases, planning of therapy, guidance of interventions. Multimodal medical image registration is the process of overlapping two or more images taken from the same scene by different modalities and different sensors. Intensity-based methods are widely used in multimodal medical image registration, these techniques register different modality images that have the same content by optimal transformation. The estimation of the optimal transformation requires the optimization of a similarity metric between the images. Recently, various optimization algorithms have been presented that the selection of appropriate optimization algorithms is very important in determining the optimal transformation parameter. The Social Spider Optimization (SSO) algorithm is one of the meta-heuristic methods that prevents premature convergence. In this paper, medical image registration technique is suggested based on the SSO algorithm. The Mutual Information (MI), Normalization of Mutual Information (NMI), and Sum of Squared Differences (SSD) are used separately as cost function (objective function) and the performance of each of these functions is checked in multimodal medical image registration. The simulation results on Brain Web data set affirm the suggested method outperforms classical registration methods in terms of convergence rate, execution time. پرونده مقاله

Image mosaicing refers to stitching two or more images that have regions overlapping with a larger and more comprehensive image. The Scale Invariant Feature Transform (SIFT) is one of the most common matching methods previously used in image mosaicing. The de-fects of SIFT are lots of mismatches, that reduce the efficiency of this algorithm. In this article, to solve this problem, a novel approach to image mosaicing is suggested. At first, the features of both images are matched based on SIFT to improve the mosaicing process. Then, the A-RANSAC algorithm suggested in [1] is employed to eliminate mismatches based on an adaptive threshold. This algorithm is used to delete incorrect matches and to improve the accuracy of images mosaicing. Image blending is the final step of mosaicing to blend the intensity of the pixels in the overlapped region to avoid the seams. The sug-gested approach of blending is based on the absolute Gaussian weighting function. The mean and variance of this function are considered as the average and variance of the data of the range of two images common to each other, respectively. The suggested blending method reduces border line in the combined images while preserving the information of the original images as much as possible, performing the mosaicing process better. The simula-tion results of the suggested image mosaicing technique, which includes the use of SIFT algorithm, A-RANSAC, and suggested image blending algorithm on the standard image databases and the created image database, show the superiority of the suggested approach according to median error criteria, precision. پرونده مقاله

Nowadays, the world needs safe and smart machines that can prevent human errors in different situations. Stress is an important factor in accidents which causes the human error. Many accidents can be prevented by identifying the stress of the driver and warning them. Due to its complexity, identifying stress in drivers is only possible by intelligent algorithms. In this paper, the Electrocardiogram (ECG) signal from drivedb dataset is used to detect stress in drivers, which has useful information that can be recorded more easily while driving. Afterwards, with a set of statistical, entropy, morphology, and chaos features, useful information is extracted from the signal. Then, in order to optimize the features, the Relief feature selector is used. Optimal features information is evaluated using Artificial Neural Networks (ANNs). Using the proposed method, the stress in drivers is detected with an accuracy of 92.6%, which has increased classification accuracy compared to recent researches. پرونده مقاله