Nawres Khlifa

Parametric images for the assessment of cardiac kinetics by magnetic resonance imaging (MRI)

The evaluation of Cardiac Magnetic Resonance (CMR) imaging exam is mainly based on the visual aspect. This visual evaluation depends on the level of expertise of the radiologist and it is characterized by variability within and between observers. The aim of this work is to propose a new method based on a mathematical model, “Fourier Transform” which calculates an amplitude parametric image. This image, calculated from the Cine MR images, allows the localization and quantification of abnormalities related to difference in contraction and their extent. The suggested amplitude image is likely to assist in the diagnosis through reducing the time taken by the radiologist to specify the abnormal contraction and by improving the accuracy of the examination. After testing this approach on patients (healthy and pathological), we have proven a good concordance between the results obtained by the parametric image and those collected from the routine examination.

Denoising of dynamic PET images using a multi-scale transform and non-local means filter

Abstract The quantification of positron emission tomography (PET) images requires a time activity curve (TAC) to provide an accurate estimation of kinetic parameters. However, the low signals to noise ratio (SNR), the important level of noise, and the low spatial resolution of PET image make the extraction of the TAC a challenging task. In this study, we present a new method based on multi-scale and non-local means method (MNLM) to reduce noise in dynamic PET sequences of small animal heart. MNLM filter takes into account the temporal correlation between images in the dynamic measurement and benefits from the complementary properties of both the Shearlet transform and the wavelet transform to provide best reduction. The method was tested on dynamic digital mouse phantom and a preclinical rat study (n = 6). Based on a comparative study with three major algorithms reviewed on the state of the art, the data analysis proved the significance of the MNLM filter. In simulated data, the major finding of the study showed that at the highest noise level (7.68%), the model gave the best result (Chi-square = 4.06). Furthermore, it presented a notable gain in terms of PSNR and SSIM plot. In real data, the MNLM showed a better result in the computation of the contrast metric with a value of 27.04 ∓ 12.1 and the highest SNR with a value of 74.38 ∓ 9.2. This approach proved a better potential and could be considered as a valuable candidate to reduce noise in clinical system.

Multi-scale and Non Local Mean based filter for Positron Emission Tomography imaging denoising

Dynamic Positron Emission Tomography (PET) is a functional imaging modality which provides information about tracer kinetic in a specific target. In the last three decades, the [18F]-fluorodeoxyglucose ([18F]-FDG) tracer has been widely used by many institutions to measure the local myocardium metabolic rate for glucose. The analysis of the dynamic measurements requires, often, parameters estimation in which the PET data is noisy. In this paper, we propose a systematic methodology to reduce noise in PET data based on the combination of an extension of Non Local Means algorithm and the Discrete Curvelet Transform. The methodology was applied to a small animal model study of the heart, where both the input function and the tissue tracer concentrations at each time were derived from de-noised images. Experimental results revealed a significant improvement in SNR and the spatial distribution of the tracer.

A comparative study of multiresolution methods to reduce the noise in scintigraphic images

This Scintigraphy represents a tool for exploring functional property shown in several pathologies. Take the example of the ventriculair ejection fraction, the renal clearance and the thyroid activity. However, scintigraphic images are strongly affected by noise. So, our objective in this work is to improve scintigraphic images to obtain sharper images and more reliable diagnosis for better orientation and understanding of pathological phenomenon. This article focuses on the comparison of the multiresolution methods for assessing the quality of scintigraphic images to reduce noise using wavelet, contourlet, curvelet, ridgelet and bandelet.

Video despeckling using Shearlet tensor-based anisotropic diffusion

Abstract This paper provides an effective method for video speckle noise reduction based on 3D Tensor-based Anisotropic Diffusion technique in the Shearlet domain (V-STAD). The proposed model exploits the multi-scale geometric representation and the sparsity property of the shearlet transform to apply a robust tensorial diffusion at each shearlet coefficients. In fact, the robustness of diffusion tensor image smoothing was not well investigated. Therefore, we adopted the robust Tukeys biweight function in the proposed tensor-based anisotropic diffusion. By this way, the filter benefits from robust statistics and sparse directional image representation property of the shearlet transform in addition to the intrinsic temporal correlations between frames to be adopted to the anisotropic nature of diffusion tensor. The experimental results demonstrate promising despeckling solution as compared to well-known state-of-the-art video denoising methods, like VBM3D and VIDOLSAT. The proposed method has clearly shown superior despeckling capability and it simultaneously demonstrated better local image structures preservation without introducing artifacts.

A priori knowledge integration for the detection of cerebral aneurysm

Abstract The detection of intracranial aneurysms is of a paramount effect in the prevention of cerebral subarachnoid hemorrhage. We propose in this paper, a new approach to detect cerebral aneurysm in digital subtraction angiography (DSA) images by fusing several sources of knowledge. After a brief description of a priori knowledge that the expert has provided about cerebral aneurysm, we propose a system architecture including fuzzy modeling and data fusion. The results on the studied cases are very promising.

Post-reconstruction-based partial volume correction methods: A comprehensive review

Abstract The vital role of positron emission tomography (PET) image is to provide an accurate quantitative information. Nevertheless, the partial volume effect (PVE) can easily alter the quantification. This effect is a consequence of the limited spatial resolution in PET which is due to the non-collinear and/or inherently random photon. For several years, great effort has been devoted to study the partial volume correction (PVC) and improve the quality of the image. At first stage, only PET modality was used and then, further study of the issue involved anatomic information from high resolution modalities in the correction process. Clearly, this regularization using segmentation, correlation and the registration step between the two modalities enhanced the performance of these techniques. These methods can be, mostly, divided into two main approaches: reconstruction-based methods and a post-reconstruction-based methods. In this paper the focus of attention was to present the most used post-reconstruction PVC methods developed in the literature. We will introduce the principal of each method, its extension and their applications in different domains.

Fall Detection for Elderly Based on Background Subtraction and Key Points Matching

The automatic detection of old persons falling in real time is a new way of interaction between Human and machines to ensure elderly people safety at home. The process of fall detection would not be possible without detecting moving persons at first, then following them and lastly recognizing and differentiating between the fall and the non-fall activity. The main aim of this paper is to propose an automatic method for fall detection based on motion detection using background subtraction, key points matching and activities recognizing.

Phantom conception for development of planar scintigraphic image restoration procedures.

The instrumentation and physiological patient factors bound to the patient in-vivo complicate the treatment of the images during a medical examination. Thus, they can contribute to the generation of artifacts in the resulting images. The artifacts degrade the quality of the images and can lead, in certain cases, to a bad diagnosis. For this reason, the appeal to the use of the simulation techniques allows to evaluate and improve the devices of acquisition and image processing. The models (called phantoms) are important tools to simulate human anatomy and physiology and to allow the evaluation of acquisition methods and image analysis. Thereby, the simulation offers a way of great importance to evaluate and improve medical techniques of acquisition devices, treatment and the reconstruction of images in-vitro.

Cerebral Vasculature Extraction using Classifier Fusion

The detection of cerebral aneurysms is of a paramount importance in the prevention of intracranial subarachnoid hemorrhage. The segmentation of intracranial vasculature presents a crucial step in the detection scheme. We propose in this paper, a new approach to extract cerebral vasculature in 2D-DSA images based on multiple classifier fusion. The classifiers used here are the FCM and the Fuzzy KNN. The main advantage of multiple classifier fusion is increasing classification efficiency and accuracy. The proposed method demostrates the contribution of fusing FCM-FKNN over the use of the individual classifier. Our method succeeded in classifying 6.23% of pixels rejected by the FCM method and 8.36% of pixels rejected by the FKNN method.