Curvature based corner detector for discrete, noisy and multi-scale contours

International audienceEstimating curvature on digital shapes is known to be a difficult problem even in high resolution images 10,19. Moreover the presence of noise contributes to the insta- bility of the estimators and limits their use in many computer vision applications like corner detection. Several recent curvature estimators 16,13,15, which come from the dis- crete geometry community, can now process damaged data and integrate the amount of noise in their analysis. In this paper, we propose a comparative evaluation of these estimators, testing their accuracy, efficiency, and robustness with respect to several type of degradations. We further compare the best one with the visual curvature proposed by Liu et al. 14, a recently published method from the computer vision community. We finally propose a novel corner detector, which is based on curvature estimation, and we provide a comprehensive set of experiments to compare it with many other classical cor- ner detectors. Our study shows that this corner detector has most of the time a better behavior than the others, while requiring only one parameter to take into account the noise level. It is also promising for multi-scale shape description

Segmentation of complex images based on component-trees: Methodological tools

International audienceComponent-trees can be used for the design of image processing methods, and in particular segmentation ones. However, despite their ability to consider various kinds of knowledge and their tractable computation, methodological deadlocks often forbid to efficiently involve them in real applications. In this article, we explore new solutions to some of these deadlocks, and more especially those related to (i) complexity of the structures of interest and (ii) multiple knowledge handling. The usefulness of the proposed strategies is illustrated by preliminary results related to vessel segmentation from 3-D angiographic data

Editorial — Special Issue: ISMM 2019

This editorial presents the Special Issue dedicated to the conference ISMM 2019 and summarizes the articles published in this Special Issue

Component-trees and multivalued images: A comparative study

International audienceIn this article, we discuss the way to derive connected operators based on the component-tree concept and devoted to multi-value images. In order to do so, we first extend the grey-level definition of the component-tree to the multi-value case. Then, we compare some possible strategies for colour image processing based on component-trees in two application fields: colour image filtering and colour document binarisation

Comparison of Discrete Curvature Estimators and Application to Corner Detection

International audienceSeveral curvature estimators along digital contours were pro- posed in recent works [1-3]. These estimators are adapted to non perfect digitization process and can process noisy contours. In this paper, we compare and analyse the performances of these estimators on several types of contours and we measure execution time on both perfect and noisy shapes. In a second part, we evaluate these estimators in the con- text of corner detection. Finally to evaluate the performance of a non curvature based approach, we compare the results with a morphological corner detector [4]

Interactive segmentation based on component-trees

International audienceComponent-trees associate to a discrete grey-level image a descriptive data structure induced by the inclusion relation between the binary components obtained at successive level-sets. This article presents an original interactive segmen- tation methodology based on component-trees. It consists of the extraction of a subset of the image component-tree, enabling the generation of a binary object which fits at best (with respect to the grey-level structure of the image) a given binary target selected beforehand in the image. A proof of the algorithmic efficiency of this methodological scheme is proposed. Concrete application examples on magnetic resonance imaging (MRI) data emphasise its actual computational efficiency and its usefulness for interactive segmentation of real images

Fast segmentation for texture-based cartography of whole slide images

In recent years, new optical microscopes have been developed, providing very high spatial resolution images called Whole Slide Images (WSI). The fast and accurate display of such images for visual analysis by pathologists and the conventional automated analysis remain challenging, mainly due to the image size (sometimes billions of pixels) and the need to analyze certain image features at high resolution. To propose a decision support tool to help the pathologist interpret the information contained by the WSI, we present a new approach to establish an automatic cartography of WSI in reasonable time. The method is based on an original segmentation algorithm and on a supervised multiclass classification using a textural characterization of the regions computed by the segmentation. Application to breast cancer WSI shows promising results in terms of speed and quality

Synthesizing Whole Slide Images

The increasing availability of digital whole slide images opens new perspectives for computer-assisted image analysis complementing modern histopathology, assuming we can implement reliable and efficient image analysis algorithms to extract the biologically relevant information. Both validation and supervised learning techniques typically rely on ground truths manually made by human experts. However, this task is difficult, subjective and usually not exhaustive. This is a well-known issue in the field of biomedical imaging, and a common solution is the use of artificial “phantoms”. Following this trend, we study the feasibility of synthesizing artificial histological images to create perfect ground truths. In this paper, we show that it is possible to generate a synthetic whole slide image with reasonable computing resources, and we propose a way to evaluate its quality

Using mathematical morphology for the anatomical labeling of vertebrae from 3D CT-scan images

In this article we propose an original method for the anatomical labeling of vertebrae from 3D CT-scan images. The primary purpose of this work is to obtain a robust referential of the abdomen. This referential can be used to locate anatomical structures like organs or blood vessels. The main problematic concerns the separation of the vertebrae, which are structures that are very close from each other. In order to detect the intervertebral spaces, we use a morphological operator which detects the dark spaces corresponding to intervertebral discs in combination with an analysis of the shape of the vertebrae in the axial plane. To reconstruct the vertebrae we use the paradigm of mathematical morphology, which consists in finding markers inside the vertebrae and compute the watershed from markers. Then we label the vertebrae according to their anatomical names. To do this, we automatically detect T12 vertebrae. We have evaluated our algorithm on 26 images

Interactive Segmentation Based on Component-trees

Component-trees associate to a discrete gray-level image a descriptive data structure induced by the inclusion relation between the binary components obtained at successive level-sets. This article presents an interactive segmentation methodology based on component-trees. It consists of the extraction of a subset of the image component-tree, enabling the generation of a binary object which fits at best (with respect to the gray-level structure of the image) a given binary target selected beforehand in the image. Compared to other interactive segmentation methods, the proposed methodology has the following advantages: (i) the segmentation result is only composed of a union of connected components of the level-sets, which ensures that no 'false contours' are included; (ii) only one image marker is needed: in particular, there is no need to give a marker for the background (contrary to some other methods); (iii) the method is fast and efficient, leading to a result computed in real-time on common image sizes