Kamel Aloui

A novel approach based brain biometrics: Some preliminary results for individual identification

Numerous anatomical studies of the human brain have shown a significant inter-individual variability of brain characteristics. Specifically, the extracted characteristics are used in our application as a biometric tool to identify individuals. For this purpose, Magnetic Resonance Imaging (MRI) images are considered. We show that using a single slice from an MRI volumetric image, acquired at a given level, one can extract significant brain codes that can be used for the purpose to identify individuals. Explicitly, the proposed biometric approach uses some coding techniques that are commonly employed for iris identification. Specifically, 1D log Gabor Wavelet has been considered for feature extraction. Finally, the proposed algorithm is evaluated on the Open Access Series of Imaging Studies (OASIS) database containing brain MRI Images. Results using 210 classes show that high accuracy of 98.25% to identify individuals are obtained.

New biometric approach based on geometrical humain brain patterns recognition: Some preliminary results

In this paper, we describe a new biometric approach based on geometrical characteristics of brain shape. Specifically, we use these geometrics characteristics as a biometric feature to identify individuals. For this purpose, Magnetic Resonance Imaging (MRI) images are considered. We show that using a single slice from an MRI volumetric image, acquired at a given level, one can extract many significant geometrical descriptors related to inter-individual variability of brain shape that can be also used to identify individuals. Explicitly, the proposed biometric approach combines two main phases. In the first phase, features extraction (FE) are achieved in order to obtain brain geometrical descriptors vector, called in this work GDB vector. A second phase is called similarity measurement (SM). Finally, the proposed algorithm is evaluated on the Open Access Series of Imaging Studies (OASIS) database containing brain MRI Images. Results using 220 classes show that high accuracy of 98.76% to identify individuals are obtained.

3D Tumor Segmentation from Volumetric Brain MR Images Using Level-Sets Method

1.1 Statement of the problem Segmentation in volumetric images is a tool allowing a diagnostics automation and as well will assist experts in quantitative and qualitative analysis. It’s an important step in various applications such as visualization, morphometrics and image-guided surgery. In the context of neuro-imaging, brain tumor segmentation from Magnetic Resonance Images (MRI) is extremely important for treatment planning, therapy monitoring, examining efficacy of radiation and drug treatments and studying the difference between healthy subjects and subjects with brain tumor. The task of manually segmentation of brain tumor from MR images is generally time-consuming and difficult. Anyway, the task is done by marking by hand the tumor regions slice-by-slice which generates set of jaggy images, so the practitioner is confronted with a succession of boundary which he mentally stacked up to be made a 3D shape of brain tumor. This shape is inevitably subjective and becomes infeasible when dealing with large data sets, also there is losing of information in the third dimension because is not taken into account in the segmentation process. All this, affect the quality and accuracy of clinical diagnosis. An automatic or semi-automatic segmentation method of brain tumor that takes entire information within the volumetric MR image into account is desirable as it reduces the load on the human raters and generates optimal segmented images (Wang & al., 2004), (Michael & al., 2001), (Lynn & al., 2001). Specially, automatic brain tumor segmentation presents many challenges and involves various disciplines such us pathology, MRI physics and image processing. Brain tumors are difficult to segment because they vary greatly in size and position, may be of any size, may have a variety of shapes and may have overlapping intensities with normal tissue and edema. This leads to numerous segmentation approaches of automatic brain tumor extraction. Low-level segmentation methods, such as pixel-based clustering, region growing, and filter-based edge detection, requires additional pre-processing and post-processing as well as considerable amounts of expert intervention and a priori knowledge on the regions of interest (ROI) (Sahoo & al., 1988). Recently, several attempts have been made to apply deformable models to brain image analysis (Moon & al., 2002). Indeed, deformable models refer to a large class of computer vision methods and have proved to be a successful segmentation technique for a wide range of applications. Deformable models, on the other hand, provide an explicit representation of the boundary and the ROI shape. They combine several desirable features such as inherent connectivity and smoothness, which counteract

Automatic detection of slow-wave sleep and REM-sleep stages using polysomnographic ECG signals

We describe in this paper a new approach of classifying the different sleep stages only by focusing on the polysomnographic ECG signals. We show the pre-processing technique of the ECG signals. At the same time the identification and elimination of the different types of artifacts which contain the signal and its reconstruction are shown. The automatic classification of the slow-deep sleep and the rapid eye movement sleep called in this work REM-sleep consists in extracting physiological indicators that characterize these two sleep stages through the polysomnographic ECG signal. In other words, this classification is based on the analysis of the cardiac rhythm during a nights sleep.

Using brain prints as new biometric feature for human recognition

Abstract Considering the evolution of neuroimaging in the medical field, some new emerging biometric modalities become interesting and promising candidates to recognize persons. These modalities are considered as a part of “Hidden Biometrics” which consists in using clinical measurements and medical imaging for recognition purpose. The main motivation in using hidden biometrics is the fact that system attacks may be extremely difficult to consider. This specificity highly contributes in increasing the robustness in terms of person verification and identification. In this paper, we deal with a novel non-invasive approach to recognize persons by extracting a brain signature, called “brainprint”. In particular, we explored the brain cortical regions of volumetric brain MRI (Magnetic Resonance Imaging) images, acquired from 220 healthy subjects. For each subject, four 3D cortical surfaces are considered, then transformed into 2D cortical folds maps. From the resulting textures, brainprints are constructed by extracting features using Wavelet Gabor Transform. These brainprints are considered in this work as a discriminative signature of the brain. In terms of performance evaluation, we show that an EER = 2.90 ± 0.47 is reached for verification mode. On the other hand, when dealing with identification, the proposed approach allows a recognition rate of 99.6%.

Selection of hyperspectral bands by adopting a dimension reduction strategy for recognition of multispectral palmprint

Palmprint is a unique and reliable biometric characteristic with high usability. Many works have been carried out on this field, during the past decades. Different algorithms and systems have been proposed and built successfully. Multispectral or hyperspectral palmprint imaging and recognition can be a potential solution to these systems because it can acquire more discriminative information for personal identity recognition. The Selection of the spectral bands is the most important step to develop the multispectral palmprint system. Most of the work done is based on methods by choosing the selected bands empirically. This work represents a preliminary study on the selection of bands by analyzing hyperspectral palmprint data (900nm ∼ 1600nm). We use an hyperspectral data provided by the “GPDShandsSWIRhyperspectral”. We adopted a dimension reduction strategy for the recognition of multispectral palmprint. We conducted a comparative study between two methods using the algorithms SOBI and JADE for the reduction of size bands. The results obtained showed that we can reduce the 20 bands chosen to 16 bands without having to modify the information from the image.

Preprocessing of biomedical signals: Removing of the baseline artifacts

In this paper, we propose a new preprocessing algorithm for biomedical signals to remove the baseline artifacts. The proposed algorithm is based on the least squares method. It can automatically estimate the form of the baseline artifacts and eliminate it. First, our algorithm consists in using the method of ordinary least squares (OLS) to estimate the slope of linear regression line. Then, we have developed a technique that allowed us to calculate the inclination angle. Finally, we eliminated these artifacts. To evaluate the reliability and robustness of our algorithm, we tested our method on biomedical signals from PHYSIOBANK database. The results obtained by our algorithm to remove the baseline artifacts set from 96.8% to 89.43%, whereas the results of the most common method consisting in applying a nonlinear median filter with a rectangular window are 64.2 to 52.38%.