Med Salim Bouhlel

Evaluation of image fusion techniques in nuclear medicine

The quality of the medical image can be evaluated by several subjective techniques. However, the objective technical assessments of the quality of medical imaging have been recently proposed. The fusion of information from different imaging modalities allows a more accurate analysis. We have developed new techniques based on the multiresolution fusion. MRI and PET images have been fused with eight multi resolution techniques. For the evaluation of fusion images obtained, we opted by objective techniques. The results prove that the fusion with RATIO and contrast techniques to offer the best results. Evaluation by objective technical quality of medical images fused is feasible and successful.

Perceptual Metrics Quality: Comparative Study for 3D Static Meshes

A 3D mesh can be subjected to different types of operations, such as compression, watermarking etc. Such processes lead to geometric distortions compared to the original version. In this context, quantifying the resultant modifications to the original mesh and evaluating the perceptual quality of degraded meshes become a critical issue. The perceptual 3D meshes quality is central in several applications to preserve the visual appearance of these treatments. The used metrics results have to be well correlated to the visual perception of humans. Although there are objective metrics, they do not allow the prediction of the perceptual quality, and do not include the human visual system properties. In the current work, a comparative study between the perceptual quality assessment metrics for 3D meshes was conducted. The experimental study on subjective database published by LIRIS / EPFL was used to test and to validate the results of six metrics. The results established that the Mesh Structural Distortion Measure metric achieved superior results compared to the other metrics.

Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition

Speech emotion recognition is the indispensable requirement for efficient human machine interaction. Most modern automatic speech emotion recognition systems use Gaussian mixture models GMM and Support Vector Machines SVM. GMM are known for their performance and scalability in the spectral modeling while SVM are known for their discriminatory power. A GMM-supervector characterizes an emotional style by the GMM parameters mean vectors, covariance matrices, and mixture weights. GMM-supervector SVM benefits from both GMM and SVM frameworks. In this paper, the GMM-UBM mean interval GUMI kernel based on the Bhattacharyya distance is successfully used. CFSSubsetEval combined with Best first algorithm and Greedy stepwise were also utilized on the supervectors space in order to select the most important features. This framework is illustrated using Mel-frequency cepstral MFCC coefficients and Perceptual Linear Prediction PLP features on two different emotional databases namely the Surrey Audio-Expressed Emotion and the Berlin Emotional speech Database.

Image encryption with dynamic chaotic Look-Up Table

In this paper we propose a novel image encryption scheme. The proposed method is based on the chaos theory. Our cryptosystem uses the chaos theory to define a dynamic chaotic Look-Up Table (LUT) to compute the new value of the current pixel to cipher. Applying this process on each pixel of the plain image, we generate the encrypted image. The results of different experimental tests, such as Key space analysis, Information Entropy and Histogram analysis, show that the proposed encryption image scheme seems to be protected against various attacks. A comparison between the plain and encrypted image, in terms of correlation coefficient, proves that the plain image is very different from the encrypted one.

Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition

Automatic Speech Emotion Recognition SER is a current research topic in the field of Human Computer Interaction HCI with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speakers utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients MFCC, Linear prediction coefficients LPC, linear prediction cepstrum coefficients LPCC, Perceptual Linear Prediction PLP and Relative Spectral Perceptual Linear Prediction Rasta-PLP features are used to characterize the emotional utterances using a combination between Gaussian mixture models GMM and Support Vector Machines SVM based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

A multi features fusion support vector machine for classification of emotion issue in the design of an audio recognition system

Most state-of-the-art automatic speech emotion recognition rely on utterance level statistics of features. In this study, spoken utterances are represented by a set of statistics from different features computed over all frames. Therefore, for exploiting the complementary emotion-specific information provided by individual features (spectral, prosodic and voice quality features), intelligent combination of features is expected. In this work, we use contour-based low-level descriptors to extract features from the emotional data and then fuse the evidences provided by these features. Finally, multi-class SVM modelling is performed directly at the output of the extracted features. The experiments were carried out on the Berlin corpus consisting of six basic emotions: sadness, boredom, neutral, fear, happiness, anger and the neutral state (no emotion). The results demonstrate that on the average, the features obtained from different information streams and combined at the decision level outperforms the single features or the features combined at the feature level in terms of classification accuracy.

A Novel Selective Encryption Scheme for Medical Images Transmission based-on JPEG Compression Algorithm

Abstract The medical imagery is a transverse activity for all medical disciplines. Its remarkable development is due to the large use of telecommunications and information technologies in the medical domain. However, since the telemedicine is a medical act that must answer to stern rules, and follow the easiness that is offered by the informatics sciences to violate the confidentiality and authenticity of medical data, the medical community is, actually, in state of opposition facing the computerization of data. To resolve this problem, a lot of methods combining compression and encryption have been developed in the literature to secure the transmission and the storage of medical images. This work presents a method of a partial or selective encryption for medical Images. It is based on the integration of the encryption in a compression process based on the Discrete Cosine Transform (DCT) in order to reduce the processing time in the encryption-decryption operation. The results of several experiments show that the proposed scheme is rapid, efficient, secure and it perfectly preserves the performances of the new one.

Discrete and continuous emotion recognition using sequence kernels

The field of automatic speech emotion recognition is a highly active and multi-diverse research area. The current state-of-the-art approach in machine analysis of human emotion has focused on recognition of discrete emotional states, such as the six basic emotion categories. However, emotion is deemed complex and is characterised in terms of latent dimensions. Accordingly, this paper aims at recognising discrete and continuous emotional states by adapting the emotional recognition system to the advanced kernel-based machine learning algorithms from the field of speaker recognition, we argue that it is more efficient in terms of recognition performance. The focus in this paper is to build a range of sequence kernel to recognise discrete and continuous emotions from the well-established real-life speech dataset (IEMOCAP) and the acted Berlin emotional speech dataset (Emo-DB).

A Fast JPEG2000 Based Crypto-Compression Algorithm: Application to the Security for Transmission of Medical Images

Over the past years, the use of telecommunications and information technologies in medicine is evolving. This involves the development of the applications bound to the telemedicine and based on a network medical image transmission. Therefore, the optimization of medical application performances remains a necessity. In this paper, we propose a novel and efficient crypto-compression algorithm. This novel scheme concerning the application of a partial encryption to the JPEG2000 file format. Our algorithm is rapid, efficient, secure and it perfectly preserves the performances of the JPEG2000 compression algorithm. In addition, the proposed transmission scheme is adapted to the Telediagnostic sector and can be easily integrated in JPEG2000 coder.

Towards the automatic evaluation of the quality of commercially-oriented Web interfaces

Usability is considered as one of the most important quality factors for commercial Web interfaces, along with others such as performance and security. However, most of usability evaluation methods for these Web applications do not support automatic measures. This paper addresses these issues through the presentation of an automatic evaluation model based on a systematic qualitative technique to validate the quality of commercially-oriented web interfaces. Thus, a case study was conducted to analyze and validate the feasibility of the approach by applying a quality model. The proposed model was based on four key usability items, which consists of 32 dimensions and 73 criteria. The results suggest that the main usability factors for commercially-oriented web interfaces are: appreciation of design, good quality of information, security/privacy of users and ease of use.