Registration and Retrieval of Elongated Structures in Medical Images

This work aims at proposing a set of methods to describe, register and retrieve images of elongated structures from a database based on their shape content. We propose a registration algorithm that jointly takes into account the gross shape of the structure and the shape of its boundary, resulting in anatomically consistent deformations. The method determines a medial axis that represents the full extent of the structure with no branches. Registration follows the linear elasticity model and is implemented through dynamic programming. Discriminative anatomic features are computed from the results of registration and used as variables in a content-based image retrieval system. A case study on the morphology of the corpus callosum in the chromosome 22q11.2 deletion syndrome illustrates the effectiveness of the method and corroborates the hypothesis that retrieval systems may also act as knowledge discovery tools.

[1]  R. Poeckert,et al.  Warping digital images using thin plate splines , 1993, Pattern Recognit..

[2]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Paul A. Yushkevich,et al.  Continuous medial representations for geometric object modeling in 2D and 3D , 2003, Image Vis. Comput..

[4]  Paul A. Yushkevich,et al.  Deformable M-Reps for 3D Medical Image Segmentation , 2003, International Journal of Computer Vision.

[5]  Jerry L. Prince,et al.  An active contour model for mapping the cortex , 1995, IEEE Trans. Medical Imaging.

[6]  Ghassan Hamarneh,et al.  Medial Profiles for Modeling Deformation and Statistical Analysis of Shape and their Use in Medical Image Segmentation , 2004, Int. J. Shape Model..

[7]  Jozef Johannes Staal.  Segmentation of elongated structures in medical images , 2004 .

[8]  Paul A. Yushkevich,et al.  Segmentation, registration, and measurement of shape variation via image object shape , 1999, IEEE Transactions on Medical Imaging.

[9]  Pheng-Ann Heng,et al.  Shape Modeling Using Automatic Landmarking , 2005, MICCAI.

[10]  E. R. Davies,et al.  Thinning algorithms: A critique and a new methodology , 1981, Pattern Recognit..

[11]  W. Eric L. Grimson,et al.  Statistical Shape Analysis Using Fixed Topology Skeletons: Corpus Callosum Study , 1999, IPMI.

[12]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[13]  James C. Gee,et al.  Corpus callosum morphology and ventricular size in chromosome 22q11.2 deletion syndrome , 2007, Brain Research.

[14]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[15]  Jordi Vitrià,et al.  Tracking elongated structures using statistical snakes , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[16]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  HARRY BLUM,et al.  Shape description using weighted symmetric axis features , 1978, Pattern Recognit..

[18]  Alberto Del Bimbo,et al.  Visual information retrieval , 1999 .

[19]  Timothy F. Cootes,et al.  Use of active shape models for locating structures in medical images , 1994, Image Vis. Comput..

[20]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[21]  James C. Gee,et al.  Volumetric, connective, and morphologic changes in the brains of children with chromosome 22q11.2 deletion syndrome: an integrative study , 2005, NeuroImage.