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21 mars 2017

Feasibility of the 3D hollow organ cartography using 2D endoscopic images

Catégorie : Doctorant

Thesis in Image Processing à the CRAN Laoratory of Nancy

Candidate profile: The candidate should ideally have a master in image processing/computer vision or a master in applied mathematics. Applications to this Ph.D thesis position of candidates with other scientific masters will also be considered.

Contact: Prof. Christian Daul, christian.daul@univ-lorraine.fr

Centre de Recherche en Automatique de Nancy (CRAN-ENSEM-UL)
2 avenue de la Forêt de Haye
F-54516 Vandoeuvre-Les-Nancy



Medical context

Endoscopy is the gold standard for the detection of cancerous or inflammatory lesions in hollow organs like the bladder (in urology the endoscopes are cystoscopes) or the stomach (in gastroscopy the endoscopes are gastroscopes). Endoscopes provide clinicians with small high resolution field of view (FoV) images. Since such limited FoV images only partially visualize regions of interest, they do not facilitate lesion diagnosis and patient follow-up. Moreover, the endoscopic video-sequences are difficult to interpret after the cystoscopy or the gastroscopy. This fact is a barrier for data archiving and examination traceability.

Scientific context (image processing)

Image mosaicing can be used to address the above mentioned medical problems. The principle of image mosaicing is to build images with an extended FoV (panoramic image or mosaic) by superimposing the common parts of the small FoV images of a video-sequence. More precisely, the image mosaicing process consists of several steps, namely i) in finding the correspondence between homologous points of image pairs, ii) in the use of this correspondence knowledge to find the geometrical link between images (image registration), iii) in the placement of all pixels or images in an unique and common mosaic coordinate system (image stitching) and iv) in the correction of texture or colour discontinuities in the mosaic. However, endoscopic data are affected by strong illumination changes (e.g. depending on the viewpoint), specular reflexions and blur, and the epithelium of the inner bladder or stomach walls have only weakly contrasted textures (see figure 1). These scene characteristics explain why endoscopic image mosaicing, and especially the point correspondence establishment step, are challenging. The CRAN laboratory has a recognized experience in endoscopic image mosaicing. Homologous point correspondence establishment was notably proposed with global [1, 2] and local [4] optical flow methods and a graph-cut [3] method.

Thesis work

Although the bi-dimensional (2D) mosaics built with the algorithms proposed at the CRAN laboratory represent a real advance in terms of diagnosis, patient follow and data archiving, the potential of image mosaicing techniques can still be better exploited. Indeed, due to the geometrical distortion occurring when placing images in 2D mosaics, large or complete organ parts cannot be entirely represented with an unique panoramic image. Moreover, only the first image of a mosaic has the original image resolution, the resolution of the other images placed in the mosaic depends on the endoscope trajectory in the hollow organ. Such problems can be avoided by building tri-dimensional (3D) mosaics (surfaces superimposed by the textures of the 2D images). 3D image mosaicing is the aim of this thesis. While the CRAN laboratory proposed a first 3D mosaicing algorithm using data acquired with a 3D cystoscope prototype [5,6], the objective of this work is to propose a 3D cartography method of hollow organs based only on data of classical endoscopes (i.e. used in clinical situation). To do so, the feasibility of Structure from Motion (SfM) and/or Simultaneous Localization and Mapping (SLAM) techniques will be studied in the case of endoscopic data. SfM and SLAM techniques determine simultaneously the surface shape and the camera (here an endoscope) trajectory using 2D image information (and intrinsic camera parameters) only. These 3D mosaicing methods are based on optimization techniques exploiting the point correspondence between 2D images.

Publications of the CRAN laboratory in the field of image mosaicing

[1] Sharib Ali, Christian Daul, Ernest Galbrun, François Guillemin and Walter Blondel, Anisotropic motion estimation on edge preserving Riesz wavelets for robust video mosaicing, Pattern Recognition, vol.51, pages 425-442, March 2016.

[2] Sharib Ali, Christian Daul, Ernest Galbrun and Walter Blondel, Illumination invariant optical flow using neighborhood descriptors, Computer Vision and Image Understanding, vol. 145, pages 95-110, April 2016.

[3] Thomas Weibel, Christian Daul, Didier Wolf, Ronald Rösch, François Guillemin. Graph based construction of textured large field of view mosaics for bladder cancer diagnosis, Pattern Recognition, vol. 45, issue 12, pages 4138-4150, 2012.

[4] Yahir Hernandez-Mier, Walter Blondel, Christian Daul Didier Wolf, François Guillemin, Fast construction of panoramic images for cystoscopic exploration, Computerized Medical Imaging and Graphics, vol. 34, issue 7, pages 579-592, 2010.

[5] Achraf Ben-Hamadou, Christian Daul and Charles Soussen, Construction of extended 3D field of views of the internal bladder wall surface: a proof of concept, 3D Research, vol. 7, issue 3, September 2016.

[6] Achraf Ben Hamadou, Charles Soussen, Christian Daul, Walter C.P.M. Blondel, Didier Wolf. Flexible calibration of structured-light systems projecting point patterns, Computer Vision and Image Understanding, vol. 117, issue 10, pages 1468-1481, 2013.

Key words

medical image processing, 3D cartography, image mosaicing, Structure from motion, SLAM


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(c) GdR 720 ISIS - CNRS - 2011-2015.