Analyses of the Watershed Transform

International audienceIn the framework of mathematical morphology, watershed transform (WT) represents a key step in image segmentation procedure. In this paper, we present a thorough analysis of some existing watershed approaches in the discrete case: WT based on flooding, WT based on path-cost minimization, watershed based on topology preservation, WT based on local condition and WT based on minimum spanning forest. For each approach, we present detailed description of processing procedure followed by mathematical foundations and algorithm of reference. Recent publications based on some approaches are also presented and discussed. Our study concludes with a classification of different watershed transform algorithms according to solution uniqueness, topology preservation, prerequisites minima computing and linearity

Deep Learning in Medical Imaging

Medical image processing tools play an important role in clinical routine in helping doctors to establish whether a patient has or does not have a certain disease. To validate the diagnosis results, various clinical parameters must be defined. In this context, several algorithms and mathematical tools have been developed in the last two decades to extract accurate information from medical images or signals. Traditionally, the extraction of features using image processing from medical data are time-consuming which requires human interaction and expert validation. The segmentation of medical images, the classification of medical images, and the significance of deep learning-based algorithms in disease detection are all topics covered in this chapter

A Deep Learning-Based Diagnosis System for COVID-19 Detection and Pneumonia Screening Using CT Imaging

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a global threat impacting the lives of millions of people worldwide. Automated detection of lung infections from Computed Tomography scans represents an excellent alternative; however, segmenting infected regions from CT slices encounters many challenges. Objective: Developing a diagnosis system based on deep learning techniques to detect and quantify COVID-19 infection and pneumonia screening using CT imaging. Method: Contrast Limited Adaptive Histogram Equalization pre-processing method was used to remove the noise and intensity in homogeneity. Black slices were also removed to crop only the region of interest containing the lungs. A U-net architecture, based on CNN encoder and CNN decoder approaches, is then introduced for a fast and precise image segmentation to obtain the lung and infection segmentation models. For better estimation of skill on unseen data, a fourfold cross-validation as a resampling procedure has been used. A three-layered CNN architecture, with additional fully connected layers followed by a Softmax layer, was used for classification. Lung and infection volumes have been reconstructed to allow volume ratio computing and obtain infection rate. Results: Starting with the 20 CT scan cases, data has been divided into 70% for the training dataset and 30% for the validation dataset. Experimental results demonstrated that the proposed system achieves a dice score of 0.98 and 0.91 for the lung and infection segmentation tasks, respectively, and an accuracy of 0.98 for the classification task. Conclusions: The proposed workflow aimed at obtaining good performances for the different system’s components, and at the same time, dealing with reduced datasets used for training

Ventricular segmentation and modeling using topological watershed transformation and harmonic state model

International audienceThis paper proposes an adapted ventricular segmentation method based on topological watershed transform. Segmentation will allow spatio-temporal modeling of trajectories of the different points belonging to the borders of the ventricle using a harmonic motion model that is able to describe such motion over the entire cardiac cycle. In addition, extraction of the adopted canonical state vector and the corresponding state equations guarantees an optimal efficacy and a gradual transition from order n to order n+1. To validate the proposed approach, an intern-image base was used. Our results show a promising ability to discern whether subjects are healthy or pathological with an 80% success rate

Imagerie temps réel : parallélisation d’algorithmes sur plate-forme multi-processeurs

Topological features of an object are fundamental in image processing. In many applications,including medical imaging, it is important to maintain or control the topology of the image. Howeverthe design of such transformations that preserve topology and geometric characteristics of the inputimage is a complex task, especially in the case of parallel processing.Parallel processing is applied to accelerate computation by sharing the workload among multipleprocessors. In terms of algorithm design, parallel computing strategies profits from the naturalparallelism (called also partial order of algorithms) present in the algorithm which provides two main resources of parallelism: data and functional parallelism. Concerning architectural design, it is essential to link the spectacular evolution of parallel architectures and the parallel processing. In effect, if parallelization strategies become necessary, it is thanks to the considerable improvements in multiprocessing systems and the rise of multi-core processors. All these reasons make multiprocessing very practical. In the case of SMP machines, immediate sharing of data provides more flexibility in designing such strategies and exploiting data and functional parallelism, notably with the evolution of interconnection system between processors.In this perspective, we propose a new parallelization strategy, called SD&M (Split Distribute andMerge) strategy that cover a large class of topological operators. SD&M has been developed in orderto provide a parallel processing for many topological transformations.Based on this strategy, we proposed a series of parallel topological algorithm (new or adaptedversion). In the following we present our main contributions:(1)A new approach to compute watershed transform based on MSF transform, that is parallel,preserves the topology, does not need prior minima extraction and suited for SMP machines.Proposed algorithm makes use of Jean Cousty streaming approach and it does not require any sortingstep, or the use of any hierarchical queue. This contribution came after an intensive study of allexisting watershed transform in the discrete case.(2)A similar study on thinning transform was conducted. It concerns sixteen parallel thinningalgorithms that preserve topology. In addition to performance criteria, we introduce two qualitativecriteria, to compare and classify them. New classification criteria are based on the relationshipbetween the medial axis and the obtained homotopic skeleton. After this classification, we tried toget better results through the proposal of a new adapted version of Couprie's filtered thinningalgorithm by applying our strategy.(3)An enhanced computation method for topological smoothing through combining parallelcomputation of Euclidean Distance Transform using Meijster algorithm and parallel Thinning–Thickening processes using the adapted version of Couprie's algorithm already mentioned.Les caractéristiques topologiques d'un objet sont fondamentales dans le traitement d'image. Dansplusieurs applications, notamment l'imagerie médicale, il est important de préserver ou de contrôlerla topologie de l'image. Cependant la conception de telles transformations qui préservent à la foi la topologie et les caractéristiques géométriques de l'image est une tache complexe, en particulier dans le cas du traitement parallèle.Le principal objectif du traitement parallèle est d'accélérer le calcul en partagent la charge de travail à réaliser entre plusieurs processeurs. Si on approche cet objectif sous l'angle de la conception algorithmique, les stratégies du calcul parallèle exploite l'ordre partiel des algorithmes, désigné également par le parallélisme naturel qui présent dans l'algorithme et qui fournit deux principales sources de parallélisme : le parallélisme de données et le parallélisme fonctionnelle.De point de vue conception architectural, il est essentiel de lier l'évolution spectaculaire desarchitectures parallèles et le traitement parallèle. En effet, si les stratégies de parallèlisation sont devenues nécessaire, c'est grâce à des améliorations considérables dans les systèmes de multitraitement ainsi que la montée des architectures multi-core. Toutes ces raisons font du calculeparallèle une approche très efficace. Dans le cas des machines à mémoire partagé, il existe un autreavantage à savoir le partage immédiat des données qui offre plus de souplesse, notamment avec l'évolution du système d'interconnexion entre processeurs, dans la conception de ces stratégies etl'exploitation du parallélisme de données et le parallélisme fonctionnel.Dans cette perspective, nous proposons une nouvelle stratégie de parallèlisation, baptisé SD&M(Split, Distribute and Merge) stratégie qui couvrent une large classe d'opérateurs topologiques.SD&M a été développée afin de fournir un traitement parallèle de tout opérateur basée sur latransformation topologique. Basé sur cette stratégie, nous avons proposé une série d'algorithmestopologiques parallèle (nouvelle version ou version adapté). Nos principales contributions sont :(1)Une nouvelle approche pour calculer la ligne de partage des eaux basée sur ‘MSF transform'.L'algorithme proposé est parallèle, préserve la topologie, n'a pas besoin d'extraction préalable deminima et adaptée pour les machines parallèle à mémoire partagée. Il utilise la même approchede calcule de flux proposé par Jean Cousty et il ne nécessite aucune étape de tri, ni l'utilisationd'une file d'attente hiérarchique. Cette contribution a été précédé par une étude intensive desalgorithmes de calcule de la ligne de partage des eaux dans le cas discret.(2)Une étude similaire sur les algorithmes d'amincissement a été menée. Elle concerne seizealgorithmes d'amincissement qui préservent la topologie. En sus des critères de performance,nous somme basé sur deux critères qualitative pour les comparer et les classés. Après cetteclassification, nous avons essayé d'obtenir de meilleurs résultats grâce avec une version adaptéede l'algorithme d'amincissement proposé par Michel Couprie.(3)Une méthode de calcul amélioré pour le lissage topologique grâce à la combinaison du calculparallèle de la distance euclidienne (en utilisant l'algorithme Meijster) et l'amincissement/épaississement parallèle (en utilisant la version adaptée de l'algorithme de Couprie déjàmentionné)

Image segmentation based upon topological operators: real-time implementation case study

International audienceIn miscellaneous applications of image treatment, thinning and crest restoring present a lot of interests. Recommended algorithms for these procedures are those able to act directly over grayscales images while preserving topology. But their strong consummation in term of time remains the major disadvantage in their choice. In this paper we present an efficient hardware implementation on RISC processor of two powerful algorithms of thinning and crest restoring developed by our team. Proposed implementation enhances execution time. A chain of segmentation applied to medical imaging will serve as a concrete example to illustrate the improvements brought thanks to the optimization techniques in both algorithm and architectural levels. The particular use of the SSE instruction set relative to the X86_32 processors (PIV 3.06 GHz) will allow a best performance for real time processing: a cadency of 33 images (512*512) per second is assured