Digital subtraction angiography (DSA) is a widely used technique for visualization of vessel anatomy in diagnosis and treatment. However, due to unavoidable patient motions, both externally and internally, the subtracted angiography images often suffer from motion artifacts that adversely affect the quality of the medical diagnosis. To cope with this problem and improve the quality of DSA images, registration algorithms are often employed before subtraction. In this paper, a novel elastic registration algorithm for registration of digital X-ray angiography images, particularly for the coronary location, is proposed. This algorithm includes a multiresolution search strategy in which a global transformation is calculated iteratively based on local search in coarse and fine sub-image blocks. The local searches are accomplished in a differential multiscale framework which allows us to capture both large and small scale transformations. The local registration transformation also explicitly accounts for local variations in the image intensities which incorporated into our model as a change of local contrast and brightness. These local transformations are then smoothly interpolated using thin-plate spline interpolation function to obtain the global model. Experimental results with several clinical datasets demonstrate the effectiveness of our algorithm in motion artifact reduction.
Optic disc or optic nerve (ON) head extraction in retinal images has widespread applications in retinal disease diagnosis and human identification in biometric systems. This paper introduces a fast and automatic algorithm for detecting and extracting the ON region accurately from the retinal images without the use of the blood-vessel information. In this algorithm, to compensate for the destructive changes of the illumination and also enhance the contrast of the retinal images, we estimate the illumination of background and apply an adaptive correction function on the curvelet transform coefficients of retinal images. In other words, we eliminate the fault factors and pave the way to extract the ON region exactly. Then, we detect the ON region from retinal images using the morphology operators based on geodesic conversions, by applying a proper adaptive correction function on the reconstructed image's curvelet transform coefficients and a novel powerful criterion. Finally, using a local thresholding on the detected area of the retinal images, we extract the ON region. The proposed algorithm is evaluated on available images of DRIVE and STARE databases. The experimental results indicate that the proposed algorithm obtains an accuracy rate of 100% and 97.53% for the ON extractions on DRIVE and STARE databases, respectively.
Seizure onset detection with minimum latency has a key role in improving the therapy studies of epilepsy. In this article, an epileptic seizure onset detection algorithm based on general tensor discriminant analysis is proposed to detect the seizure through EEG signals with smallest delay before the development of clinical symptoms. In this algorithm, seizure and nonseizure EEG signal epochs are exhibited by spectral, spatial, and temporal domains (third-order tensors) in wavelet decomposition. Then, to reduce feature space, projection matrices are extracted from tensor-represented EEG signal by general tensor discriminant analysis. In this strategy, the discriminative information in the training tensors is preserved that it is a benefit in comparison with common feature space reduction algorithms such as principal component analysis and multilinear subspace analysis. The proposed seizure onset detection algorithm is evaluated on 44 epileptic patients from 2 standard datasets and recognizes 98% of seizures with average delay of 4.5 seconds. The obtained results show efficiency and effectiveness of our proposed algorithm in comparison with other algorithms.
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