Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, ...is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately.
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•Automatic selection of the Localized Region-based Active Contour Model (LRACM).•Statistical moment-based features as image descriptors.•Automatic Brain tumor segmentation framework.•LRACM performance depends on the image content.•Fast and reliable MRI data analysis.
In this paper, a method of vascular structure identification in intraoperative 3D Contrast-Enhanced Ultrasound (CEUS) data is presented. Ultrasound imaging is commonly used in brain tumor surgery to ...investigate in real time the current status of cerebral structures. The use of an ultrasound contrast agent enables to highlight tumor tissue, but also surrounding blood vessels. However, these structures can be used as landmarks to estimate and correct the brain shift. This work proposes an alternative method for extracting small vascular segments close to the tumor as landmark. The patient image dataset involved in brain tumor operations includes preoperative contrast T1MR (cT1MR) data and 3D intraoperative contrast enhanced ultrasound data acquired before (3D-iCEUS(start) and after (3D-iCEUS(end) tumor resection. Based on rigid registration techniques, a preselected vascular segment in cT1MR is searched in 3D-iCEUS(start) and 3D-iCEUS(end) data. The method was validated by using three similarity measures (Normalized Gradient Field, Normalized Mutual Information and Normalized Cross Correlation). Tests were performed on data obtained from ten patients overcoming a brain tumor operation and it succeeded in nine cases. Despite the small size of the vascular structures, the artifacts in the ultrasound images and the brain tissue deformations, blood vessels were successfully identified.
•An alternative Active Contour Model solution for medical images is introduced.•A multi-population Cuckoo Search Strategy (MCSS) is implemented to boost ACM.•Proposed method was applied on Magnetic ...Resonance Imaging (MRI) data.•MCSS outperforms traditional ACM and ACM driven by multi-population PSO.
In this paper, an alternative Active Contour Model (ACM) driven by Multi-population Cuckoo Search (CS) algorithm is introduced. This strategy assists the converging of control points towards the global minimum of the energy function, unlike the traditional ACM version which is often trapped in a local minimum. In the proposed methodology, each control point is constrained in a local search window, and its energy minimisation is performed through a Cuckoo Search via Lévy flights paradigm. With respect to local search window, two shape approaches have been considered: rectangular shape and polar coordinates. Results showed that the CS method using polar coordinates is generally preferable to CS performed in rectangular shapes. Real medical and synthetic images were used to validate the proposed strategy, through three performance metrics as the Jaccard index, the Dice index and the Hausdorff distance. Applied specifically to Magnetic Resonance Imaging (MRI) images, the proposed method enables to reach better accuracy performance than the traditional ACM formulation, also known as Snakes and the use of Multi-population Particle Swarm Optimisation (PSO) algorithm.
This paper presents a Localized Active Contour Model (LACM) integrating an additional step of background intensity compensation. The region-based active contour models that use statistical intensity ...information are more sensitive to the high mean intensity distance between consecutive regions. In Magnetic Resonance Imaging (MRI) this distance is great between the foreground and the background, hence it leads to an incorrect delineation of the target. In order to resolve this problem, an automatic process is introduced in our model for balancing the mean intensity distance between an image foreground and its background. The aim is to minimize the attraction effect of the active contour model to the undesired borderlines defined by these two mentioned image regions. By using this approach not only the obtained accuracy outperforms the traditional localized mean separation active contour model, but also it reduces the computation time of the segmentation task. In addition, this method was efficiently applied on automatic brain tumor segmentation in multimodal MRI data. The Hierarchical Centroid Shape Descriptor (HCSD) was used for detecting the region of interest i.e. abnormal tissue so as to automatically initialize the active contour. The validation of experiments was carried out on synthetic images and the quantitative evaluation was performed on the BRATS2012 database. Finally, the accuracy achieved by the proposed method was compared to the localized mean separation intensity, the localized Chan-Vese, the local Gaussian distribution fitting and the local binary fitting models by using the Dice coefficient, Sensitivity, Specificity and the Hausdorff distance. The computation time of the methods was also measured for comparison purposes. The obtained results show that the proposed model outperforms the accuracy of the selected state of the art methods. Moreover, it is also faster than the comparative methods in the medical image segmentation task.
Robust conics detection and extraction have received an increased interest due to the potential applications in many critical tasks. In medical imaging, the conics can help to detect optic disk ...abnormalities in retinographic images, or cranial sections and bone structures in radiography, magnetic resonance, and computed tomography images. Moreover, the physicians can be guided in the prostate cancer diagnosis by assessing the size and shape of the prostate in ultrasound or magnetic resonance images. Some of these structures are composed of discontinuous points, imprecise directions, multiple bifurcations, and variable line–trace width, or may present undesired noise, which makes the curve fitting hard to compute. Image acquisition also plays a fundamental role in adding artifacts, as unexpected outliers, which jeopardizes the applicability and efficiency of the current methods. This article proposes a strategy for determining the general equation of oblique conics in noisy and sparse data sets. As a novelty, this methodology employs four uncorrelated points from a randomly sampled population to estimate the initial space search. Then, Differential Evolution (DE) method iterates until the solution that best fits the oblique conic section is found. Advantages of the method include a DE implementation using a weighted fitness function based on the Mean Squared Error (MSE) and reinforced with the Random Sample Consensus (RANSAC) inliers measurement idea. This objective function allows associating the fitting error to the number of points into the inliers-region. Additionally, the number of parameters to tune is reduced and almost generalized. A distinctive feature is related to the eccentricity, which could conduct to a variety of rich solutions. However, some disadvantages have arisen trying to identify a specific conic. For instance, when some parabolas are considered, the method could evolve optimally to an ellipse. The proposed methodology has been evaluated using synthetic and real images. These images were contaminated with additive white Gaussian noise to prove the method stability. Numerical results showed that the proposed method is a reliable and fast technique for computing any conic from scattered data. Results obtained from two dissimilar applications prove that the proposed method can be used in a variety of sensitive applications. Those applications include the optic disk segmentation and traffic sign classification. The assessment showed an improvement of 2% across the metrics for detecting the optic disk and reached a classification accuracy of 0.9730 for the speed limit traffic signs.
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•Oblique conics detection and extraction in disperse data.•Surrogate modeling using differential evolution algorithm.•Traffic sign recognition improved by oblique conics detection.•Optic disk extraction in retinographical images.•General framework for optimal curve fitting.
Purpose
Intraoperative ultrasound (iUS) imaging is commonly used to support brain tumor operation. The tumor segmentation in the iUS images is a difficult task and still under improvement because of ...the low signal-to-noise ratio. The success of automatic methods is also limited due to the high noise sensibility. Therefore, an alternative brain tumor segmentation method in 3D-iUS data using a tumor model obtained from magnetic resonance (MR) data for local MR–iUS registration is presented in this paper. The aim is to enhance the visualization of the brain tumor contours in iUS.
Methods
A multistep approach is proposed. First, a region of interest (ROI) based on the specific patient tumor model is defined. Second, hyperechogenic structures, mainly tumor tissues, are extracted from the ROI of both modalities by using automatic thresholding techniques. Third, the registration is performed over the extracted binary sub-volumes using a similarity measure based on gradient values, and rigid and affine transformations. Finally, the tumor model is aligned with the 3D-iUS data, and its contours are represented.
Results
Experiments were successfully conducted on a dataset of 33 patients. The method was evaluated by comparing the tumor segmentation with expert manual delineations using two binary metrics: contour mean distance and Dice index. The proposed segmentation method using local and binary registration was compared with two grayscale-based approaches. The outcomes showed that our approach reached better results in terms of computational time and accuracy than the comparative methods.
Conclusion
The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.
Dans cet article, nous décrivons tout d'abord le principe général de la modélisation dynamique dont l'examen de l'efficacité dans l'analyse du trafic des réseaux multiservices constitue l'un des ...objets principaux de cette étude. En montrant comment cette technique peut étre appliquée pour obtenir le systéme élémentaire markovien: de la file d'attente M/M/1/œ; nous répondons en partie a la grande problématique sur la gestion de la QoS dans le cas d'un nœud isolé puis d'un réseau. Nous nous intéressons ensuite aux principes du contróle de trafic des réseaux multiservices, en nous inspirant de travaux existants dans la littérature et de politiques de gestion des phénoménes de congestion défini dans les recommandations I.371 de l'UIT-T et ATM-Forum UNI spécification V3.1. Comme solution possible a cette problématique, nous proposons l'hypothése de la réaction a la congestion par compression adaptative du trafic, en cas d'apparition d'un phénoméne de congestion dépassant toute prédiction. Le progrés de cette hypothése aura le mérite de resurgir et accroitre l'intérét du recours a la compression comme solution a la problématique de congestion, en particulier pour les réseaux sans fil; étant donné que les ressources radio sont rares et partagées.
This work focuses on the design and implementation of a computer system capable of tracking the position of a vehicle in real time and recording its different positions during a journey. Thanks to ...such a system, the owner of the vehicle will be kept informed of the various past locations of his vehicle but also of other additional information which will be provided to him by the system, for example the total distance traveled by the vehicle, etc. To succeed in designing such a system, we used two technologies: GPS and GSM (GPRS). GPS is a technology that, thanks to a constellation of satellites orbiting the earth, allows us to obtain the geographical location of a place (or geographical coordinates including latitude, longitude and altitude). GSM, on the other hand, refers to the cellular network which serves as a transmission medium for conveying the geographical coordinates freshly collected. Concretely, the vehicle will be equipped with an embedded system consisting of an Arduino card, a GPS chip and a GSM/GPRS expansion card. This on-board system will send the geographical coordinates to a computer server in which is installed a database intended to store this data. Thanks to a web application linked to this database, the owner will be able to track his vehicle.
The dark and worrying health picture characterized by the revelations made by international organizations concerning pneumonia challenged us as researchers. It is in this perspective that we decided ...to make our contribution to this problem by presenting a system for pneumonia detection in chest x-ray images. To achieve this goal, we used an approach based on deep learning to properly identify pathological or non-pathological radiographs by setting up a convolutional neural network called Xception. Two optimization algorithms were selected, namely Adam and SGD. The setting of hyperparameters of our convolutional neural network led us to a promising result compared to the size of our dataset. In conclusion, the obtained results in our experiments showed that the SGD optimization algorithm reached the best result of 92% accuracy on new data with a learning rate of 0.001 for 20 epochs.
Intraoperative ultrasound (iUS) imaging is routinely performed to assist neurosurgeons during tumor surgery. In particular, the identification of the possible presence of residual tumors at the end ...of the intervention is crucial for the operation outcome. B-mode ultrasound remains the standard modality because it depicts brain structures well. However, tumorous tissue is hard to differentiate from resection cavity borders, blood and artifacts. On the other hand, contrast enhanced ultrasound (CEUS) highlights residuals of the tumor, but the interpretation of the image is complex. Therefore, an assistance system to support the identification of tumor remnants in the iUS data is needed. Our approach is based on image segmentation and data fusion techniques. It consists of combining relevant information, automatically extracted from both intraoperative B-mode and CEUS image data, according to decision rules that model the analysis process of neurosurgeons to interpret the iUS data. The method was tested on an image dataset of 23 patients suffering from glioblastoma. The detection rate of brain areas with tumor residuals reached by the algorithm was qualitatively and quantitatively compared with manual annotations provided by experts. The results showed that the assistance tool was able to successfully identify areas with suspicious tissue.
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