Incremental Local Linear Fuzzy Classifier in Fisher Space

Optimizing the antecedent part of neurofuzzy system is an active research topic, for which different approaches have been developed. However, current approaches typically suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neurofuzzy systems that are structured based on local linear classifiers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neurofuzzy classifier is then built in the transformed space, starting from the simplest form (a global linear classifier). If the overall performance of the classifier was not satisfactory, it would be iteratively refined by incorporating additional local classifiers. In addition, rule consequent parameters are optimized using a local least square approach. Our refinement strategy is motivated by LOLIMOT, which is a greedy partition algorithm for structure training and has been successfully applied in a number of identification problems. The proposed classifier is compared to several benchmark classifiers on a number of well-known datasets. The results prove the efficacy of the proposed classifier in achieving high performance while incurring low computational effort

Application of Model-Based Estimation to Time-Delay Estimation of Ultrasonic Testing Signals

Time-Delay-Estimation (TDE) has been a topic of interest in many applications in the past few decades. The emphasis of this work is on the application of model-based estimation (MBE) for TDE of ultrasonic signals used in ultrasonic thickness gaging. Ultrasonic thickness gaging is based on precise measurement of the time difference between successive echoes which reflect back from the back wall of the test piece. The received echoes are modelled by Gaussian pulses and the desired system response is estimated using Gauss-Newton and Space Alternating Generalized Expectation Maximization (SAGE) algorithms. In addition to the model-based estimation approach, five other TDE techniques including peak-to-peak measurement, cross-correlation, cross-correlation with interpolation, phase-slope, and cross-correlation with Wiener filtering are also considered and compared with the SAGE. The main advantage of the SAGE algorithm, in addition to its higher accuracy, is its ability to deconvolve the overlapping echoes

CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans

Segmentation of lung tissue in computed tomography (CT) images is a precursor to most pulmonary image analysis applications. Semantic segmentation methods using deep learning have exhibited top-tier performance in recent years, however designing accurate and robust segmentation models for lung tissue is challenging due to the variations in shape, size, and orientation. Additionally, medical image artifacts and noise can affect lung tissue segmentation and degrade the accuracy of downstream analysis. The practicality of current deep learning methods for lung tissue segmentation is limited as they require significant computational resources and may not be easily deployable in clinical settings. This paper presents a fully automatic method that identifies the lungs in three-dimensional (3D) pulmonary CT images using deep networks and transfer learning. We introduce (1) a novel 2.5-dimensional image representation from consecutive CT slices that succinctly represents volumetric information and (2) a U-Net architecture equipped with pre-trained InceptionV3 blocks to segment 3D CT scans while maintaining the number of learnable parameters as low as possible. Our method was quantitatively assessed using one public dataset, LUNA16, for training and testing and two public datasets, namely, VESSEL12 and CRPF, only for testing. Due to the low number of learnable parameters, our method achieved high generalizability to the unseen VESSEL12 and CRPF datasets while obtaining superior performance over Luna16 compared to existing methods (Dice coefficients of 99.7, 99.1, and 98.8 over LUNA16, VESSEL12, and CRPF datasets, respectively). We made our method publicly accessible via a graphical user interface at medvispy.ee.kntu.ac.ir

Early Visual Processing of Feature Saliency Tasks: A Review of Psychophysical Experiments

The visual system is constantly bombarded with information originating from the outside world, but it is unable to process all the received information at any given time. In fact, the most salient parts of the visual scene are chosen to be processed involuntarily and immediately after the first glance along with endogenous signals in the brain. Vision scientists have shown that the early visual system, from retina to lateral geniculate nucleus (LGN) and then primary visual cortex, selectively processes the low-level features of the visual scene. Everything we perceive from the visual scene is based on these feature properties and their subsequent combination in higher visual areas. Different experiments have been designed to investigate the impact of these features on saliency and understand the relative visual mechanisms. In this paper, we review the psychophysical experiments which have been published in the last decades to indicate how the low-level salient features are processed in the early visual cortex and extract the most important and basic information of the visual scene. Important and open questions are discussed in this review as well and one might pursue these questions to investigate the impact of higher level features on saliency in complex scenes or natural images

Effectiveness of "rescue saccades" on the accuracy of tracking multiple moving targets: An eye-tracking study on the effects of target occlusions

Occlusion is one of the main challenges in tracking multiple moving objects. In almost all real-world scenarios, a moving object or a stationary obstacle occludes targets partially or completely for a short or long time during their movement. A previous study (Zelinsky & Todor, 2010) reported that subjects make timely saccades toward the object in danger of being occluded. Observers make these so-called "rescue saccades" to prevent target swapping. In this study, we examined whether these saccades are helpful. To this aim, we used as the stimuli recorded videos from natural movement of zebrafish larvae swimming freely in a circular container. We considered two main types of occlusion: object-object occlusions that naturally exist in the videos, and object-occluder occlusions created by adding a stationary doughnut-shape occluder in some videos. Four different scenarios were studied: (1) no occlusions, (2) only object-object occlusions, (3) only object-occluder occlusion, or (4) both object-object and object-occluder occlusions. For each condition, two set sizes (two and four) were applied. Participants' eye movements were recorded during tracking, and rescue saccades were extracted afterward. The results showed that rescue saccades are helpful in handling object-object occlusions but had no reliable effect on tracking through object-occluder occlusions. The presence of occlusions generally increased visual sampling of the scenes; nevertheless, tracking accuracy declined due to occlusion

L' intégration d'information bas et haut-niveau pour la segmentation optimisée d'images cérébrales 3D chez l'enfant nouveau-né

La première étape de cette Thèse était de créer un atlas probabiliste du cerveau néonatal comprenant un atlas 'template' et des modèles probabilistes du cerveau, du liquide cérébro-spinal (CSF) et du crâne. L'atlas est basé sur les images IRM T1 en haute résolution de 7 patients d âge gestationnel compris entre 39 et 42 semaines. L'atlas 'template' a été évalué par la détermination de la déviation de points de repère anatomiques caractéristiques et la somme total de déformation locale nécessaire pour la normalisation des tissus cérébraux en fonction d une image néonatale de référence. Dans la deuxième partie, nous avons construit un simulateur d images IRM cérébrales néonatales à partir de notre fantôme 3D néonatal numérique. Ce fantôme est composé de 9 types tissulaires différents: scalpe, crâne, graisse, muscle, dure-mère, substance grise, substance blanche, myelinisée et non-myelinisée et liquide cérébrospinal. Le fantôme numérique a été utilisé pour caractériser les intensités des signaux pour simuler ensuite les images IRM. Les images simulées avec une dégradation bien contrôlée peuvent servir comme données d'évaluation pour des méthodes d'analyse des images IRM néonatales, tel que des algorithmes de segmentation et/ou d acquisition. Dans la dernière partie, nous avons développé une méthode de segmentation tissulaire automatique pour les IRM néonatales. Dans cette étude, nous avons appliqué un algorithme basé sur un atlas permettant la segmentation du crâne, du cerveau, et du CSF chez le nouveau-né à partir des images IRM 3D en T1. Nous avons utilisé la méthode de segmentation basée sur l'algorithme EM et la chaîne aléatoire de Markov qui est implémentée et utilisée dans l'outil SPM et sa boîte à outils VBM en conjonction avec notre atlas probabiliste, qui est utilisé pour constituer des informations a priori. Les résultats démontrent que notre méthode permet de segmenter avec une grande précision le cerveau, le CSF et le crâne des IRM néonatales.AMIENS-BU Santé (800212102) / SudocSudocFranceF