Integration of Fuzzy Mathematical Morphology and Fuzzy Spatial Relationships into ITK

International audience<p>The aim of this paper is to present the integration of new filters computing fuzzy mathematical morphology and spatial relationshipsinto ITK and to share our experience with the Open source community.Our implementation architecture follows the essential system conceptsof ITK. An illustrative application is provided to help ITK users anddevelopers to evaluate the capabilities of these filters.</p

Towards Interpretability of Segmentation Networks by analyzing DeepDreams

International audience<p>Interpretability of a neural network can be expressed as the identification of patterns or features to which the network can be either sensitive or indifferent. To this aim, a method inspired by DeepDream is proposed, where the activation of a neuron is maximized by performing gradient ascent on an input image. The method outputs curves that show the evolution of features during the maximization.A controlled experiment show how it enables assess the robustness to a given feature, or by contrast its sensitivity. The method is illustrated on the task of segmenting tumors in liver CT images.</p

J Math Imaging Vis (2009) 34: 107–136 DOI 10.1007/s10851-009-0136-3 A New Fuzzy Connectivity Measure for Fuzzy Sets And Associated Fuzzy Attribute Openings

Abstract Fuzzy set theory constitutes a powerful representation framework that can lead to more robustness in problems such as image segmentation and recognition. This robustness results to some extent from the partial recovery of the continuity that is lost during digitization. In this paper we deal with connectivity measures on fuzzy sets. We show that usual fuzzy connectivity definitions have some drawbacks, and we propose a new definition that exhibits better properties, in particular in terms of continuity. This definition leads to a nested family of hyperconnections associated with a tolerance parameter. We show that corresponding connected components can be efficiently extracted using simple operations on a max-tree representation. Then we define attribute openings based on crisp or fuzzy criteria. We illustrate a potential use of these filters in a brain segmentation and recognition process. This work has been partly supported by a grant from the Nationa

Automatic Knww Meniscus Tear Detection and Orientation Classification with Mask-RCNN

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Kindey Cortex Segmentation in 2D CT with U-Nets Ensemble Aggregation

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