RESUMEN

This paper proposes a 3D face alignment of 2D face images in the wild with noisy landmarks. The objective is to recognize individuals from their single profile image. We first proceed by extracting more than 68 landmarks using a bag of features. This allows us to obtain a bag of visible and invisible facial keypoints. Then, we reconstruct a 3D face model and get a triangular mesh by meshing the obtained keypoints. For each face, the number of keypoints is not the same, which makes this step very challenging. Later, we process the 3D face using butterfly and BPA algorithms to make correlation and regularity between 3D face regions. Indeed, 2D-to-3D annotations give much higher quality to the 3D reconstructed face model without the need for any additional 3D Morphable models. Finally, we carry out alignment and pose correction steps to get frontal pose by fitting the rendered 3D reconstructed face to 2D face and performing pose normalization to achieve good rates in face recognition. The recognition step is based on deep learning and it is performed using DCNNs, which are very powerful and modern, for feature learning and face identification. To verify the proposed method, three popular benchmarks, YTF, LFW, and BIWI databases, are tested. Compared to the best recognition results reported on these benchmarks, our proposed method achieves comparable or even better recognition performances.

RESUMEN

Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture has the potential to merge both the local and global contextual information with reduced weights. To overcome the data heterogeneity, we proposed a preprocessing technique based on intensity normalization and adaptive contrast enhancement of MRI data. Furthermore, for an effective training of such a deep 3D network, we used a data augmentation technique. The paper studied the impact of the proposed preprocessing and data augmentation on classification accuracy.Quantitative evaluations, over the well-known benchmark (Brats-2018), attest that the proposed architecture generates the most discriminative feature map to distinguish between LG and HG gliomas compared with 2D CNN variant. The proposed approach offers promising results outperforming the recently supervised and unsupervised state-of-the-art approaches by achieving an overall accuracy of 96.49% using the validation dataset. The obtained experimental results confirm that adequate MRI's preprocessing and data augmentation could lead to an accurate classification when exploiting CNN-based approaches.

Asunto(s)

Neoplasias Encefálicas , Glioma , Encéfalo , Neoplasias Encefálicas/diagnóstico por imagen , Glioma/diagnóstico por imagen , Humanos , Imagen por Resonancia Magnética , Redes Neurales de la Computación

RESUMEN

Three sensitive,selective and reproducible stability-indicating methods are presented for determination of nitazoxanide (NTZ),a new anti-protozoal drug,in presence of its degradation products.Method A utilizes the first derivative of ratio spectra spectrophotometry by measurement of the amplitude at 364.4 nm using one of the degradation products as a divisor.Method B is a chemometric-assisted spectrophotometry,where principal component regression (PCR) and partial least squares (PLS) were applied.These two approaches were successfully applied to quantify NTZ in presence of degradation products using the information included in the absorption spectra in the range 260-360 nm.Method C is based on the separation of NTZ from its degradation products followed by densitometric measurement of the bands at 254 nm.The separation was carried out on silica gel 60F254,using chloroform-methanol-ammonia solution-glacial acetic acid (95∶5∶1∶1 by volume,pH=5.80) as a developing system.These methods are suitable as stability-indicating methods for the determination of NTZ in presence of its degradation products either in bulk powder or in pharmaceutical formulations.Statistical analysis of the results has been carried out revealing high accuracy and good precision.