Fingerprint Recognition in Forensic Scenarios.

Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime.

Multispectral Facial Recognition in the Wild.

This work proposes a multi-spectral face recognition system in an uncontrolled environment, aiming to identify or authenticate identities (people) through their facial images. Face recognition systems in uncontrolled environments have shown impressive performance improvements over recent decades. However, most are limited to the use of a single spectral band in the visible spectrum.

Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units.

Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed.

Enhanced Classification of Interstitial Lung Disease Patterns in HRCT Images Using Differential Lacunarity.

The analysis and interpretation of high-resolution computed tomography (HRCT) images of the chest in the presence of interstitial lung disease (ILD) is a time-consuming task which requires experience. In this paper, a computer-aided diagnosis (CAD) scheme is proposed to assist radiologists in the differentiation of lung patterns associated with ILD and healthy lung parenchyma. Regions of interest were described by a set of texture attributes extracted using differential lacunarity (DLac) and classical methods of statistical texture analysis.

Algorithm versus physicians variability evaluation in the cardiac chambers extraction.

Congenital heart diseases are present in eight of every 1000 newborns. The diagnosis of those pathologies usually depends on the available imaging methods. A correct diagnosis requires a detailed observation of the heart chambers, wall motions, valves function, and quantitative evaluation of the cavity volumes.

A method for corneal nerves automatic segmentation and morphometric analysis.

The segmentation and morphometric analysis of corneal sub-basal nerves, from corneal confocal microscopy images, has gained recently an increased interest. This interest arises from the possibility of using changes in these nerves as the basis of a simple and non-invasive method for early detection and follow-up of peripheral diabetic neuropathy, a major cause of chronic disability in diabetic patients. Here, we propose one method for automatic segmentation and analysis of corneal nerves from images obtained in vivo through corneal confocal microscopy.

Fast volumetric registration method for tumor follow-up in pulmonary CT exams.

An oncological patient may go through several tomographic acquisitions during a period of time, needing an appropriate registration. We propose an automatic volumetric intrapatient registration method for tumor follow-up in pulmonary CT exams. The performance of our method is evaluated and compared with other registration methods based on optimization techniques.

Phase symmetry approach applied to children heart chambers segmentation: a comparative study.

Segmentation of echocardiographic images presents a great challenge because these images contain strong speckle noise and artifacts. Besides, most ultrasound segmentation methods are semi-automatic, requiring initial contour to be manually identified in the images. In this work, we propose an algorithm based on the phase symmetry approach and level set evolution, in order to extract simultaneously all heart cavities in a fully automatic way.

Image denoising methods for tumor discrimination in high-resolution computed tomography.

Pixel accuracy in images from high-resolution computed tomography (HRCT) is ultimately limited by reconstruction error and noise. While for visual analysis this may not be relevant, for computer-aided quantitative analysis in either densitometric, or shape studies aiming at accurate results, the impact of pixel uncertainty must be taken into consideration. In this work, we study several denoising methods: geometric mean filter, Wiener filtering, and wavelet denoising.

Quantitative evaluation of a pulmonary contour segmentation algorithm in X-ray computed tomography images.

Rationale And Objectives: Pulmonary contour extraction from thoracic x-ray computed tomography images is a mandatory preprocessing step in many automated or semiautomated analysis tasks. This study was conducted to quantitatively assess the performance of a method for pulmonary contour extraction and region identification.

Materials And Methods: The automatically extracted contours were statistically compared with manually drawn pulmonary contours detected by six radiologists on a set of 30 images.