Oral epithelial dysplasia is a common precancerous lesion type that can be graded as mild, moderate and severe. Although not all oral epithelial dysplasia become cancer over time, this premalignant condition has a significant rate of progressing to cancer and the early treatment has been shown to be considerably more successful. The diagnosis and distinctions between mild, moderate, and severe grades are made by pathologists through a complex and time-consuming process where some cytological features, including nuclear shape, are analysed. The use of computer-aided diagnosis can be applied as a tool to aid and enhance the pathologist decisions. Recently, deep learning based methods are earning more and more attention and have been successfully applied to nuclei segmentation problems in several scenarios. In this paper, we evaluated the impact of different color spaces transformations for automated nuclei segmentation on histological images of oral dysplastic tissues using fully convolutional neural networks (CNN). The CNN were trained using different color spaces from a dataset of tongue images from mice diagnosed with oral epithelial dysplasia. The CIE L*a*b* color space transformation achieved the best averaged accuracy over all analyzed color space configurations (88.2%). The results show that the chrominance information, or the color values, does not play the most significant role for nuclei segmentation purpose on a mice tongue histopathological images dataset.

For videos to be streamed, they have to be coded and sent to users as signals that are decoded back to be reproduced. This coding-decoding process may result in distortion that can bring differences in the quality perception of the content, consequently, influencing user experience. The approach proposed by Bosse et al. [1] suggests an Image Quality Assessment (IQA) method using an automated process. They use image datasets prelabeled with quality scores to perform a Convolutional Neural Network (CNN) training. Then, based on the CNN models, they are able to perform predictions of image quality using both Full- Reference (FR) and No-Reference (NR) evaluation. In this paper, we explore these methods exposing the CNN quality prediction to images extracted from actual videos. Various quality compression levels were applied to them as well as two different video codecs. We also evaluated how their models perform while predicting human visual perception of quality in scenarios where there is no human pre-evaluation, observing its behavior along with metrics such as SSIM and PSNR. We observe that FR model is able to better infer human perception of quality for compressed videos. Differently, NR model does not show the same behaviour for most of the evaluated videos.

This paper describes a technique for an automatic polar map creation from myocardial perfusion SPECT images. This exam is widely used in post-infarction patient evaluations, in order to foretell the outcome and left ventricular function. This exam is difficult to interpret, since it is a 3D representation of the heart. Use of polar maps intends to simplify analysis of the exam, converting the 3D image into a 2D plot. The technique developed is based on a combination of image registration and feature detection. For this study, an overall of 31 cases were tested, with the results compared with the gold standard software. The correlation calculated between techniques was 0.76 in the worst case and 0.98 in the best case. Index Terms: Image Registration, Myocardial Infarction, Polar Map, SPECT.