No abstract

No abstract

BackgroundThe process of medical image fusion is combining two or more medical images such as Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) and mapping them to a single image as fused image. So purpose of our study is assisting physicians to diagnose and treat the diseases in the least of the time.MethodsWe used Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) as input images, so fused them based on combination of two dimensional Hilbert transform (2-D HT) and Intensity Hue Saturation (IHS) method. Evaluation metrics that we apply are Discrepancy (Dk) as an assessing spectral features and Average Gradient (AGk) as an evaluating spatial features and also Overall Performance (O.P) to verify properly of the proposed method.ResultsIn this paper we used three common evaluation metrics like Average Gradient (AGk) and the lowest Discrepancy (Dk) and Overall Performance (O.P) to evaluate the performance of our method. Simulated and numerical results represent the desired performance of proposed method.ConclusionsSince that the main purpose of medical image fusion is preserving both spatial and spectral features of input images, so based on numerical results of evaluation metrics such as Average Gradient (AGk), Discrepancy (Dk) and Overall Performance (O.P) and also desired simulated results, it can be concluded that our proposed method can preserve both spatial and spectral features of input images.

In this study, we have introduced an accurate retinal images registration method using affine moment invariants (AMI's) which are the shape descriptors. First, some closed-boundary regions are extracted in both reference and sensed images. Then, AMI's are computed for each of those regions. The centers of gravity of three pairs of regions which have the minimum of distances are selected as the control points. The region matching is performed by the distance measurements of AMI's. The evaluation of region matching is performed by comparing the angles of three triangles which are built on these three-point pairs in reference and sensed images. The parameters of affine transform can be computed using these three pairs of control points. The proposed algorithm is applied on the valid DRIVE database. In general (for the case, each sensed image is produced by rotating, scaling, and translating the reference image with different angles, scale factors, and translation factors), the success rate and accuracy is 95 and 96 %, respectively.

No abstract

The objective of this study is to propose an accurate emotion recognition methodology. To this end, a novel fusion framework based on wavelet transform (WT), and matching pursuit (MP) algorithm was offered. Electrocardiogram (ECG) and galvanic skin response (GSR) of 11 healthy students were collected while subjects listened to emotional music clips. In both fusion techniques, Coiflet wavelet (Coif5 at level 14) was chosen as a wavelet family and MP dictionary, respectively. After employing the proposed fusion framework, some statistical measures were extracted. To describe emotions, three schemes were adopted: two-dimensional model (five classes), valence-(three classes), and arousal-(three classes) based emotion categories. Subsequently, the probabilistic neural network (PNN) was applied to classify affective states. The experiments indicate that the MP-based fusion approach outperform the wavelet-based fusion technique or methods using only ECG or GSR indices. Considering the proposed fusion techniques, the maximum classification rate of 99.64% and 92.31% was reached for the fusion methodology based on the MP algorithm (five classes of emotion) and wavelet-based fusion technique (three classes of valence), respectively.

No abstract

At the present time, imaging guided renal biopsy is used to provide diagnoses in most types of primary and secondary renal diseases. It has been claimed that renal biopsy can provide a link between diagnosis of renal disease and its pathological conditions. However, sometimes there is a considerable mismatch between patient renal outcome and pathological findings in renal biopsy. This is the time to address some new diagnostic methods to resolve the insufficiency of conventional percutaneous guided renal biopsy. Nanotechnology is still in its infancy in renal imaging; however, it seems that it is the next step in renal biopsy, providing solutions to the limitations of conventional modalities.