Publications Scientifiques
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Item Segmentation and detection of the retinal vascular network using fast filtering(Inder science, 2023) Rahmoune, Nabila; Rahmoune, AdelChanges in retinal blood vessels are a characteristic sign of many retinal diseases. Therefore, the automatic segmentation of vessels is an essential element for the diagnosis of different ocular diseases. In this paper, we present a novel algorithm for the detection and the segmentation of the vascular network of blood vessels in fundus images. Our algorithm employs two mean linear filters using the convolutional kernel, one directional along a line and the second on a square region, in combination with thresholding. The proposed approach’s performance was tested on the public datasets DRIVE and STARE. Based on the test results, the mean segmentation accuracy, sensitivity, specificity and time complexity of retinal images in DRIVE are 94.27%, 97.01%, 66.20% and 1.63 s and for the STARE database, they are 93.41%, 95.54%, 66.55% and 2.13 s respectively. The proposed algorithm is simple and very fast. It achieved satisfactory mean segmentation accuracy with very low time complexityItem Blood cells image segmentation and counting using deep transfer learning(IEEE, 2023) Gharbi, Aghiles; Neggazi, Mohamed Lamine; Touazi, Faycal; Gaceb, Djamel; Yagoubi, Mohamed RiadIn this paper, we present a two-step automatic blood cell counting approach for accurately and efficiently determining the complete blood count (CBC). The approach involves using two convolutional neural networks (CNNs) for the segmentation of red blood cells, white blood cells, and platelets, and then applying three different algorithms (Watershed, Connected Component Labeling, and Circle Hough Transform) to count the cells present in the masks produced by the CNNs. We also introduce a loss function for the Circle Hough Transform algorithm to further improve its accuracy. Our approach shows good results compared to other methods in the literature and has the potential to significantly reduce the time and effort required for manual blood cell countingItem Multi-agent medical image segmentation : a survey(Elsevier, 2023) Bennai, Mohamed Tahar; Guessoum, Zahia; Mazouzi, Smaine; Cormier, Stéphane; Mezghiche, MohamedDuring the last decades, the healthcare area has increasingly relied on medical imaging for the diagnosis of a growing number of pathologies. The different types of medical images are mostly manually processed by human radiologists for diseases detection and monitoring. However, such a procedure is time-consuming and relies on expert judgment. The latter can be influenced by a variety of factors. One of the most complicated image processing tasks is image segmentation. Medical image segmentation consists of dividing the input image into a set of regions of interest, corresponding to body tissues and organs. Recently, artificial intelligence (AI) techniques brought researchers attention with their promising results for the image segmentation automation. Among AI-based techniques are those that use the Multi-Agent System (MAS) paradigm. This paper presents a comparative study of the multi-agent approaches dedicated to the segmentation of medical images, recently published in the literatureItem Optimization of matched filters for VHR image segmentation(Taylor and Francis, 2022) Semcheddine, Belkis Asma; Daamouche, AbdelhamidThe availability of very high resolution remotely sensed images has made the use of spectral information alone insufficient for class recognition, so the integration of spatial information into the segmentation task becomes necessary. Spatial information takes into account the surrounding of a pixel, rather than dealing with a pixel as an isolated item. In this paper, inspired by matched filtering theory, we propose a new technique for spatial feature extraction. The technique consists of using 2D kernels convolved with the spectral bands. The convolution operation bears valuable spatial information about the pixel under consideration. The concatenation of the extracted spatial features and spectral features of pixels feeds a support vector machine (SVM) to segment the image of interest. To cope with the complexity of images having objects of varying sizes (i.e. urban areas), we adopted a hierarchical strategy. That is, kernels with increasing size are applied to extract different spatial features corresponding to different objects. Thus, we have extended the use of matched filters from single object enhancement to multi-object enhancement. A challenging step in designing the matched filters is the two-dimensional kernels coefficients selection, which we formulated as an optimization problem within a particle swarm optimization framework. The optimization process was driven by two different fitness functions, the cross-validation SVM accuracy and the Bhattacharyya distance, which were both evaluated on training samples. We assessed the proposed procedure on two very high-resolution images having different spatial resolutions. The obtained results showed significant improvement in terms of the overall classification accuracy (over 10% for both images). Moreover, visual inspection of the segmented images, in addition to pepper and salt elimination, revealed significant improvement in many objects not detected by the spectral method. Our method of extracting spatial features seems to be very efficientItem A cooperative approach based on local detection of similarities and discontinuities for brain MR images segmentation(2020) Bennai, Mohamed Tahar; Smaine, Mazouzi; Guessoum, Zahia; Mezghiche, Mohamed; Cormier, StephaneThis paper introduces a new cooperative multi-agent approach for segmenting brain Magnetic Resonance Images (MRIs). MRIs are manually processed by human radiology experts for the identification of many diseases and the monitoring of their evolution. However, such a task is time-consuming and depends on expert decision, which can be affected by many factors. Therefore, various types of research were and are still conducted to automate MRI processing, mainly MRI segmentation. The approach presented in this paper, without any parametrization or prior knowledge, uses a set of situated agents, locally interacting to segment images according to two main phases: the detection of discontinuities and the detection of similarities. An implementation of this approach was tested on phantom brain MR images to assess the results and prove its efficiency. Experimental results ensure a minimum of 89% Dice coefficient with increasing values of the noise and the intensity non-uniformityItem Abnormal tissus extraction in MRI brain medical images(IEEE, 2011) Cherifi, Dalila; Doghmane, Mohamed Zinelabidine; Nait-Ali, A.; Aici, Zakia; Bouzelha, SalimThis study is a comparison between two image segmentation's methods; the first method is based on normal brain's tissue recognition then tumor extraction using thresholding method. The second method is classification based on EM segmentation which is used for both brain recognition and tumor extraction. The goal of these methods is to detect, segment, extract, classify and measure properties of the brain normal and abnormal (tumor) tissues