Computerized Medical Imaging and Graphics, oct. 2011

1. Background and motivation
2. Quick facts about the special issue
3. Scanning the special issue
3.1. Invited review papers
3.2. Included papers
3.2.1. Image acquisition
3.2.2. Image registration
3.2.3. Image processing and analysis
Acknowledgments
References

IEEE transactions on Image Processing, juin 2011

Mathematical morphology (MM) offers a wide range of operators to address various image processing problems. These operators can be defined in terms of algebraic (discrete) sets or as partial differential equations (PDEs). In this paper, we introduce a nonlocal PDEs-based morphological framework defined on weighted graphs. We present and analyze a set of operators that leads to a family of discretized morphological PDEs on weighted graphs. Our formulation introduces nonlocal patch-based configurations for image processing and extends PDEs-based approach to the processing of arbitrary data such as nonuniform high dimensional data. Finally, we show the potentialities of our methodology in order to process, segment and classify images and arbitrary data.

Inverse Problems and Imaging (IPI), vol. 5(2), p. 511-530, American Institute of Mathematical Sciences, mai 2011

This article proposes a new framework to regularize imaging linear inverse problems using an adaptive non-local energy. A non-local graph is optimized to match the structures of the image to recover. This allows a better reconstruction of geometric edges and textures present in natural images. A fast algorithm computes iteratively both the solution of the regularization process and the non-local graph adapted to this solution. The graph adaptation is efficient to solve inverse problems with randomized measurements such as inpainting random pixels or compressive sensing recovery. Our non-local regularization gives state-of-the-art results for this class of inverse problems. On more challenging problems such as image super-resolution, our method gives results comparable to sparse regularization in a translation invariant wavelet frame.

International Journal of Human-Computer Studies, avr. 2011

The development of customized image processing applications is time consuming and requires high level skills. This paper describes the design of an interactive application generation system oriented towards producing image processing software programs. The description is focused on two models which constitute the core of the human–computer interaction. First, the formulation model identifies and organizes information that is assumed necessary and sufficient for developing image processing applications. This model is represented as a domain ontology which provides primitives for the formulation language. Second, the interaction model defines ways to acquire such information from end-users. The result of the interaction is an application ontology from which a suitable software is generated. This model emphases the gradual emergence of a semantics of the problem through purely symbolic representations. Based on these two models, a prototype system has been implemented to conduct experiments.

Computerized Medical Imaging and Graphics, mars 2011

In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images. The proposed segmentation uses a multi-resolution approach which reproduces the slide examination done by a pathologist. Each resolution level is analyzed with a focus of attention resulting from a coarser resolution level analysis. At each resolution level, a spatial refinement by label regularization is performed to obtain more accurate segmentation around boundaries. The proposed segmentation is fully unsupervised by using domain specific knowledge.

International Journal of Pattern Recognition and Artificial Intelligence, Vol. 24, No. 8, pp. 1181-1208, avr. 2010

This paper investigates what kinds of information are necessary and sufficient to design and evaluate image processing software programs and proposes a representation of these information elements using a computational language performable by vision systems and understandable by experts. The language is built upon a formulation model which distinguishes the specification of a goal and the definition of an input image class. Goals are stated in terms of tasks together with result samples. Image classes are defined by both linguistic and iconic descriptions. The model is implemented as an OWL domain ontology which provides the primitives for the formulation language.