Alima Damak Masmoudi

LBPV descriptors-based automatic ACR/BIRADS classification approach

Mammogram tissue density has been found to be a strong indicator for breast cancer risk. Efforts in computer vision of breast parenchymal pattern have been made in order to improve the diagnostic accuracy by radiologists. Motivated by recent results in mammogram tissue density classification, a novel methodology for automatic American College of Radiology Breast Imaging Reporting and Data System classification using local binary pattern variance descriptor is presented in this article. The proposed approach characterizes the local density in different types of breast tissue patterns information into the LBP histogram. The performance of macro-calcification detection methods is developed using FARABI database. Performance results are given in terms of receiver operating characteristic. The area under curve of the corresponding approach has been found to be 79%.

A new human identification based on fusion fingerprints and faces biometrics using LBP and GWN descriptors

Single modality biometric recognition system is often not able to meet the desired system performance requirements. Several studies have shown that multimodal biometric identification systems improve the recognition accuracy and allow performances that are required for many security applications. In this paper, we have developed a multimodal biometric recognition system which combines two modalities: face and fingerprint. For face trait, we build features based on Gabor Wavelet Networks (GWNs), while Local Binary Patterns (LBP) is used for fingerprint trait. Experimental results affirm that a weighted sum based fusion achieves excellent recognition performances, which out performs both single biometric systems.

Hand vein recognition system with circular difference and statistical directional patterns based on an artificial neural network

In this article, a novel hand vein pattern recognition process for human identification is presented. Hand vein characteristics can be considered as more reliable in biometric domain compared with other biometric characteristics, such as palmprint and fingerprint, because veins are located in volume, making features more robust to test conditions. In this paper, a rotation invariant texture descriptor called Circular Difference and Statistical Directional Patterns (CDSDP) is proposed to extract hand vein patterns. Its histogram is considered as attribute vector. The CDSDP is a surrounding circular difference with weights incorporating the statistical directional information of vessels. Experimental results show that the proposed descriptor based on CDSDP has better performance than the previous descriptors used in local binary patterns (LBP). The proposed method gives an Identification Rate (IR) of 99.8 % and an Error Equal Rate (EER) of 0.01 %. Furthermore, the average processing time of the proposed method is 5.2ms for one hand vein posture, which satisfies the criterion of a real time hand vein recognition system.

Diagnosis of masses in mammographic images based on Zernike moments and local binary attributes

Masses are important elements in the diagnosis of breast cancer. Many studies discussed the problem of detection and/or diagnosis of masses and most of these researches were based on shape descriptors to make decision. Textural descriptors contribute in indicating the presence of masses. Morphological descriptors determine their malignancy degree. Thus, we decided in our work to make a combination of morphological and textural descriptors. In fact, this method allowed us to extract different features in order to help make a decision concerning the malignancy of masses. The shape descriptor “Zernike moments” has the advantages to be invariant to the rotation and to be orthogonal. In addition, the texture descriptor “local binary attributes” provides information about the local variations of gray levels in the image. A multi-layer perceptron is used in the classification stage. The results were validated by using 160 regions of interest which are extracted from the database of mammographic images DDSM (Digital Database for Screening Mammography). We obtained an area under the ROC (Receiver Operating Characteristics) curve which is equal to 0,96. The results were confirmed by a radiologist.

Implementation of a Fingerprint Recognition System Using LBP Descriptor

Conventional fingerprint recognition systems provide authentication by a direct matching of minutiae points and orientation field. Although several resemblance algorithms have been proposed, reliable automatic fingerprint verification remains a challenge due to the difficulty in alignment for direct matching and the construction of adequate functions for resemblance measurements. In this paper, we propose a solution to the aforementioned problems using a local binary pattern (LBP) descriptor applied to minutiae and orientation fields. The experimental results on the public fingerprint database, Fingerprint Verification Competition (FVC), show high recognition rates. The proposed system was implemented on the platform known as FPGA Virtex-II Xilinix™ (Virtex2p-xc2vp7-FF672) and optimized with respect to hardware resources occupation, based on a co-design methodology. All the proposed algorithms are involved in the design of a mixed software/hardware dedicated system. A classifier based on pulse mode neural networks using floating-point format is proposed.

An efficient microcalcifications detection based on dual spatial/spectral processing

Microcalcifications are tiny deposits of calcium located in breast tissue. They appeared as very small highlighted regions in comparison with their surrounding tissue. Spatial non linear enhancement can be applied for microcalcification detection. However, efficiency of a such approach depends on breast density: in case of extreme breast density, the contrast between microcalcification’s details and their surrounding tissue is attenuated leading to a limitation of spatially based approaches. In that case, frequency analysis such as wavelet based analysis can be more relevant for dissociating microcalcifications. The main goal of Computer Aided Detection systems (CAD) is to detect breast cancer at an early stage for all breast density classes by using entropies to enhance and then detect microcalcification details. Accordingly, we combine our approach a spatial Automatic Non Linear Stretching (ANLS) and Shannon Entropy based Wavelet Coefficient Thresholding (SE_WCT). Validation of the proposed approach is done on the Mammographic Image Analysis Society (MIAS) database. The evaluation of the contrast is based on the Second-Derivative-Like measure of enhancement(SDME). Accordingly, it yields to a mean SDME of 78.8dB on the whole database. The performance metric for evaluating our proposed CAD is the Receiver Operating Characteristic(ROC) curve and the free-response ROC (FROC). An area under the ROC curve Az = 0.92 is obtained as well as 97.14 % of True Positives (TP) with 0,48 False positives per image (FP).

A Novel Biometric System Based Hand Vein Recognition

As a reliable and universal biometric characteristic, hand vein identification has attracted many interested researchers. The hand vein identification system exhibits several excellent advantages in the biometric domain because it meets the increasing demand of accuracy and robustness. In this paper, we propose a new biometric recognition system based on hand vein features. The detection and extraction of the region of interest is based on Voronoi Decomposition. Furthermore, contrast enhancement is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Starting with the basic Gaussian Matched Filter (GMF) and its variant, we propose a new technique called the Improved Gaussian Matched Filter method (IMPGMF) surmounting the false detection of hand vessels with the traditional GMF. Feature points are then detected based on ending and bifurcation structures in the image map obtained with the proposed IMPGMF method and taken as signature for our biometric system. Then, Artificial Neural Networks (ANN) are used for the classification step. In the validation step, we used a 1500 hand vein image from the BOSPHORUS database. The Error Equal Rate is 0.01 % and the Area Under curve of the corresponding system is approximately 0.98, demonstrating a very high security level.

A new Tsallis based automatic non linear enhancement of mammograms for microcalcifications segmentation in high density breast

Microcalcifications are tiny deposits of calcium located in breast tissue. They appeared as very small highlighted regions in comparaison with their surrounding tissue. The difference of contrast between microcalcifications and the normal tissue depend on the breast density: The more the breast is dense, the less is the contrast. In this context, we propose to enhance microcalcifications details for each type of breast density using for methods. As we know that the BIRADS/ACR 4 contains dense breast, Thats why we have proposed to make the Non Linear Stratching (NLS)automatic by applying an improved Tsallis entropy. The proposed mammography enhancement approach is evaluated on the Digital Database for Screening Mammography (DDSM) database.

Robust mass classification–based local binary pattern variance and shape descriptors

In several countries, breast cancer is a serious public health problem. Computer–Aided Detection and Diagnosis (CAD) systems have been used with relative success aiding healthcare professionals. The goal of such approach technique is contribute on the radiologist task aiding in the detection of different types of cancer at an early stage. This paper presents a methodology for masses detection on mammographic images based on the Local Binary Pattern Variance (LBPV) and shape descriptors. Classification of these structures is accomplished through Artificial Neural Network (ANN), which separate them in two groups: masses and non–masses. The performance of macro–calcification detection methods is developed using FARABI database. Performance results are given in terms of receiver operating characteristic.

A bi-modal palmvein palmprint biometric human identification based on fusing new CDSDP features

Uni-modal analysis of palmprint and palmvein has been investigated for human recognition. One of the problems encountered with such system is that the Uni-modal biometric is less perfect, reliable and vulnerable to spoofing, as the data can be imitated or forged. In this paper, we present a multi-modal Personal identification system using palmprint and palmvein images with their fusion applied at feature level. The feature vectors of palmprint and palmvein images are extracted with Circular Difference and Statistical Directional Patterns descriptor. Experimental results show that the proposed system provides a better representation and achieves lower error rates in palm recognition. Furthermore, the proposed multimodal method outperforms any of its individual modality.