Automatic Identification of Regions of Interest on Renal Tomographic Images. Identification Automatique des Régions D’Intérêts sur des Images Tomographiques Rénales

Automatic Identification of Regions of Interest on Renal Tomographic Images

Identification Automatique des Régions D’Intérêts sur des Images Tomographiques Rénales

Djamal Boukerroui Wala Touhami  Jean-Pierre Cocquerez 

Université de Technologie de Compiègne, CNRS UMR 6599 Heudiasyc BP 20529 - 60205 Compiègne Cedex, France

École Nationale d’Ingénieurs de Tunis BP 37, Le Belvedere 1002 Tunis

18 June 2008
30 June 2009
| Citation



We propose, in this paper, an original approach in a statistical framework, for fully automatic delineation of kidneys (healthy and pathological) in 2D CT images. Our approach has two main steps : a localisation step followed by a delineation step. The localisation step is guided by a statistically learned prior spatial model in one hand and a grey level prior model in a second hand. The second step, utilizes the localisation results in order to precisely delineate the kidney’s regions using a set of learned IF-THEN rules. The proposed approach is tested on clinically acquired images and promising results are obtained.


Nous proposons, dans le présent papier, une approche originale dans un cadre statistique pour l’identification automatique des reins (sains et pathologiques) sur des images tomographiques bidimensionnelles (CT). Notre approche est constituée de deux phases : une phase de localisation suivie d’une phase de délimitation. La phase de localisation est guidée, d’une part, par un modèle a priori spatial et d’autre part, par un modèle a priori sur les niveaux de gris, statistiquement appris. La seconde phase consiste à utiliser les résultats de la localisation afin de délimiter la région du rein en utilisant un ensemble de règles. Cette approche est testée sur des images cliniquement acquises et des résultats satisfaisants sont obtenus.


Automatic detection, statistical approach, prior models, kidney cysts, CT.

Mots clés

Détection automatique, approche statistique, modèles a priori, kyste de rein, CT.

1. Introduction
2. Architecture Globale de l’Approche Proposée
3. Phase de Localisation des Reins
4. Phase de Délimitation du Rein
5. Résultats
6. Discussion des Cas d’Échec
7. Conclusion

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