UOLO - automatic object detection and segmentation in biomedical images

We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.Comment: Publised on DLMIA 2018. Licensed under the Creative Commons CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0

Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm

Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis

An Efficient Divide-and-Conquer Algorithm for Morphological Filters

Morphological filters, evolved from the traditional envelope filter, are function oriented filtration techniques. A recent research on the implementation of morphological filters was based on the theoretical link between morphological operations and the alpha shape. However the Delaunay triangulation on which the alpha shape method depends is costly for large areal data. This paper proposes a divide-and-conquer method as an optimization to the alpha shape method aiming to speed up its performance. The large areal surface is divided into small sub-surfaces so that the alpha shape method is executed on the partitioned surfaces in a fast manner. The contact points are searched on each sub-surface and merged into a super set on which the alpha shape method is applied again to archive the updated result. The recursion process is repeated until the contact points of the whole surface are obtained. The morphological envelope could be computed recursively without the 3D Delaunay triangulation to the whole surface data. Meanwhile this method retains almost all the merits of the alpha shape method. The experiment shows that the result obtained by the divide-and-conquer algorithm is consistent with the one generated by applying the alpha shape method directly. The performance evaluation reveals that the divide-and-conquer algorithm achieved superior performances over the original alpha shape method

2: Optimal structures in graph segmentation

Review of a work submitted to the International Symposium on Mathematical Morphology, 8 (ISMM)

Développement de méthodes de traitement d'images pour la détermination de paramètres variographiques locaux

La géostatistique fournit de nombreux outils pour caractériser et traiter des données réparties dans l'espace. La plupart de ces outils sont basés sur l'analyse et la modélisation d'une fonction appelée variogramme qui permet de construire différents opérateurs spatiaux : le krigeage et les simulations. Les modèles variographiques sont relativement intuitifs : certains paramètres variographiques peuvent être directement interprétés en termes de caractéristiques structurales. Ces approches sont cependant limitées car elles ne permettent pas de prendre correctement en compte la structuration locale des données. Plusieurs types de modèles géostatistiques non-stationnaires existent. Ils requièrent généralement un paramétrage compliqué, peu intuitif, et ils n'apportent pas de réponse satisfaisante quant à certains de types de non-stationnarité. C'est pour répondre au besoin d'une prise en compte efficace et opérationnelle de la non-stationnarité dans un jeu de données que, dans le cadre de cette thèse, nous prenons le parti de déterminer des paramètres variographiques locaux, appelés M-Paramètres par des méthodes de traitement d'images. Notre démarche se fonde principalement sur la détermination des paramètres morphologiques de dimensions et d'orientations de structures. Il résulte de la détermination de M-Paramètres une meilleure adéquation entre modèles variographiques et caractéristiques structurales des données. Les méthodes de détermination de M-Paramètres développées ont été appliquées sur des données bathymétriques, sur des jeux de données laissant apparaître des corps géologiques complexes ou encore sur des jeux de données environnementaux, liés au domaine de la pollution en zone urbaine par exemple. Ces exemples illustrent les améliorations de résultats de traitement géostatistique obtenus avec M-Paramètres. Enfin, partant du constat que certains phénomènes ne respectent pas une propagation euclidienne, nous avons étudié l'influence du choix de la distance sur les résultats de krigeage et de simulation. En utilisant des distances géodésiques, nous avons pu obtenir des résultats d'estimation impossible à reproduire avec des distances euclidiennes.PARIS-MINES ParisTech (751062310) / SudocSudocFranceF

2: Watershed by image foresting transform, tie-zone, and theoretical relationships with other watershed definitions

Review of a work submitted to the International Symposium on Mathematical Morphology, 8 (ISMM)

1: Oversegmentation control for inexact graph matching: first results

Review of a work submitted to the International Symposium on Mathematical Morphology, 8 (ISMM)

Analyse et optimisation des surfaces des chemises de moteurs thermiques

PARIS-MINES ParisTech (751062310) / SudocSudocFranceF