Compression d'images adaptée aux angiographies coronaires pour le stockage sur carte à mémoire optique

Compression d'images adaptée aux angiographies coronaires pour le stockage sur carte à mémoire optique

Adaptive Image Compression Algorithm for Angiograms Stored on Optical Memory Card

Hugues Benoit-Catin Atilla Baskurt  Denis Delamarre  Rémy Prost 

CREATIS, Unité de Recherche CNRS (UMR 5515), affiliée à l'INSERM INSA, Bat . 502, 69621 Villeurbanne Cedex

Département d'Information Médicale (DIM), CHRU de Rennes

Page: 
433-443
|
Received: 
23 July 1997
|
Accepted: 
N/A
|
Published: 
31 October 1998
| Citation

OPEN ACCESS

Abstract: 

The main objective of the Cardio-Média project is to produce a coronarian multimedia data record stored on an optical card in order to offer a better follow-up for the patients treated by angioplasty . In this paper, we present the compression algorithm implemented to store the angiographìc images of the data record . This algorithm is based on a wavelet decomposition followed by an adapted lattice quantization of the wavelet coefficients . An original bit allocation algorithm is used during a learning step in orderto provide a fast coding algorithm which is adapted to the angiographic images. A subjective evaluation of the diagnostic quality of the images, based on the consensus approach leads to a compression ratio of 12 :1 which insures both a sufficient medical quality and a sufficient data compression in regards to the storage capacity of the optical card.

Résumé

Le projet Cardio-Média a pour objectif la création d'un prototype de dossier coronarien sur carte optique afin de faciliter le suivi clinique des patients traités par angioplastie . Dans cet article, nous présentons l'algorithme de compression mis en oeuvre et les résultats obtenus . Notre algorithme utilise une transformation en ondelettes et une quantification vectorielle adaptée des coefficients d'ondelettes . Son originalité repose sur la phase d'apprentissage qui permet de disposer d'un algorithme de compression/décompression rapide adapté à la modalité médicale « angiographie ». Une évaluation subjective par consensus de la qualité diagnostique des images comprimées a permis de retenir un taux de compression de 12 qui répond aux contraintes matérielles et médicales du projet.

Keywords: 

Image coding, medical imaging, angiography, subband coding, pyramidal lattice quantization, diagnostic quality assessment

Mots clés

Compression d'images, imagerie médicale, angiographie, compression sous-bandes, quantificateur vectoriel sur treillis, évaluation de la qualité diagnostique

1. Introduction
2. Matériels Et Méthodes
3. Résultats Et Discussions
4. Conclusion
5. Remerciements
6. Annexe
  References

[1] A.N . Akansu, R.A. Haddad, Multiresolution signal decomposition, New-York : Academic Press Inc., 1992, 376 p.

[2] N. Akrout, Contribution à la compression d'images par quantification vectorielle : algorithmes et circuit intégré spécifique, Thèse de Doctorat, INSA Lyon, spécialité Dispositifs de l'Electronique Intégrée, Janvier 1995, 205 p .

[3] P.A . Angelidis, MR image compression using a wavelet transform coding algorithm, Magnetic Resonance Imaging, July 1994, Vol . 12, N°7, p . 1111 -1120 .

[4] M . Antonini, M . Barlaud, P. Mathieu, I . Daubechies, Image coding using wavelet transform, IEEE Trans. on Image Processing, April 1992, Vol. 1, N°2, p . 205-220.

[5] O. Baudin, Schéma de compression adaptative d'images : modélisation paramétrique et classification, quantification et codage, évaluation . Application en imagerie médicale, Thèse de Doctorat, INSA Lyon, spécialité Génie Biologique et Médical, Mars 1996 .

[6] O . Baudin, A . Baskurt, T. Moll, R. Prost, D . Revel, F. Ottes, M . Khamadja, M . Amiel, ROC assessment of compressed wrist radiographs, European Journal of Radiology, March 1996, Vol . 22, p .228-231 .

[7] A . Baskurt, I. Magnin, R. Goutte, Adaptive discrete cosine transform coding algorithm for digital mammography, Optical Engineering, September 1992 , Vol . 31, N°9, p . 1922-1928 .

[8] K. Belloulata, A . Baskurt, H . Benoit-Cattin, Y. Ding, R. Prost, Fractal coding of medical images, in Proc . of SPIE Medical Imaging, Newport Beach, February 1996, Vol . 2707, p . 598-609 .

[9] H . Benoit-Cattin, Compression sous-bandes : contribution à la décomposition par filtres FIR et à la quantification des sous-bandes . Application en imagerie médicale 2D et 3D, Thèse de Doctorat, INSA Lyon, spécialité Signal Image Parole, Novembre 1995, 258 p .

[10] P. Beretta, R . Prost, Unsharp masking and its inverse processing integrated in a compression decompression scheme . Application to cardiac angiograms, in Proc. SPIE Medical Imaging, San Diego, February 1995, Vol . 2431, p . 233 -244 .

[11] P. Beretta, Compression d'images appliquée aux angiographies cardiaques : aspects algorithmiques, évaluation de la qualité diagnostique, Thèse deDoctorat, INSA Lyon, spécialité Génie Biologique et Médical, soutenance prévue pour Novembre 1997 .

[12] E .W. Bouldin, R . Callen, The optical memory card : A portable medical record, Journal of Medical System, 1990, Vol . 14, p . 154-157 .

[13] R . Castagno, R . Lancini, O . Egger, A comparison of different quantization strategies for subband coding of medical images, in Proc. SPIE Medical Imaging, Newport Beach, February 1996, Vol . 2707, p . 227-238 .

[14] V. Chameroy, R . Di Paola, Toward multidimensional medical image coding , in Proc. SPIE Medical Imaging, Newport Beach, February 1992, Vol . 1653 , p . 261-272.

[15] K .K . Chan, C .C . Lau, S-L . Lou, A. Hayrapetian, K .B .T. Ho, H .K . Huang, Three dimensional transform compression of images from dynamic studies , in Proc. SPIE Medical Imaging, Newport Beach, February 1990, Vol . 1232, p . 322-326.

[16] J .H . Conway, N .J.A Sloane, Fast quantizing and decoding algorithms for lattice quantizers and codes, IEEE Trans. on Information Theory, March 1982, Vol . IT-28, N°2, p .227-232 .

[17] V. Cordonnier, Développement et perpectives de la carte à microprocesseur, Gestions Hospitalières, 1992, Vol. 317, p . 482-484 .

[18] P.C . Cosman, C . tseng, R.M . gray, R .A. Olshen, L .E . Moses, H .C. Davidson, C .J . Bergin, E .A . Riskin, Tree structured vector quantization of CT chest scans : Image quality and diagnostic accuracy, IEEE Trans. on Medical Imaging, November 1993, Vol . 12, N°4, p . 727-739.

[19] R .E . Crochiere, S .A . Webber, J .L . Flanagan, Digital coding of speech in subbands, Bell Syst. Tech . Journal, 1976, Vol. 55, p . 1069-1085 .

[20] J-C .Culioli, Introduction à1' optimisation, Paris : Edition Marketing, Ellipses , 1994, 316 p .

[21] C . Diab, Compression d'images par codage spatial de sous-bandes décomposées par transformations orthogonales, Thèse de Doctorat, INSA Lyon, spécialité Acoustique, Novembre 1992, 284 p.

[22] Dicom 3 : Publication for Data Compression Standards, Washington DC : Nema Publication, 1993, Vol . PS-3 .

[23] Y. Ding, O . Baudin, P. Beretta, R . Prost, Medically adapted JPEG compression scheme, in Proc. of SPIE Medical Imaging, San Diego, February 1995, Vol .2431, p . 516-525 .

[24] P. Fedi, Les professionnels de la santé et la carte à mémoire, Gestions hospitalières, 1992, Vol . 317, p . 479-482.

[25] T.R .fischer,Apyramidvectorquantizer,IEEETrans .onInformationTheory , July 1986, Vol . 32, N°4, p . 568-583 .

[26] A . Gersho, Asymptotically optimal block quantization, IEEE Trans. on Information Theory, July 1979, Vol . 25, N°4, p . 237-245 .

[27] A . Gersho, On the structure of vector quantizers, IEEE Trans. on Information Theory, Mars 1982, Vol . IT-28, p . 157-166 .

[28] W. Good, S. Lattner, G. Maitz, Evaluation of image compression using plausible non visually weigthed image fidelity measures, in Proc. SPIE Medical Imaging, Newport Beach, February 1996, Vol. 2707, p . 301-309 .

[29] H. Guibert, A . Gamache, Optical memory card applicability for implementing a portable medical record, Med-Inf-Lond, July-September 1993, Vol. 18, N°3 , p . 271-278 .

[30] B .K .T. Ho, P. Saipetch, J . Wei, M. Ma, J. Villasenor, M-J . Tsai, Video compression algorithm for dynamic angiographie images, in Proc. SPIE Medical Imaging, Newport Beach, February 1994, Vol . 2164, p . 302-309 .

[31] W-L. Hsu, H . Derin, Spatial and frequency decomposition for image compression, in Proc . SPIE Medical Imaging, San Diego, February 1995 , Vol . 2431, p. 623-634 .

[32] T.H . Karson, S . Chandra, A. Morehead, S .E. Nissen, J.D . Thomas, Digital compression of echocardiographic images : Is it viable?, in Proc. Computers in Cardiology, London, September 1993, p . 831-834 .

[33] S . Lattner, W. Good, G . Maitz, Visually weighted assesment of image degradation resulting from image compression, in Proc. SPIE Medical Imaging , Newport Beach, February 1996, Vol . 2707, p . 507-518 .

[34] H . Lee, Y. Kim, A .H . Rowbey, E .A . Riskin, Statistical distributions of DCT coefficients and their application to an interframe compression algorithm for 3D medical images, IEEE Trans . on Medical Imaging, September 1993, Vol . 12, N°3, p . 478-485 .

[35] D . LE Gall, The MPEG video compression algorithm, Signal Processing : Image Communication, Vol . 4, N°2, April 1992, p. 129-140 .

[36] J. Lienard, Compression réversible d'images angiographiques numérisées , Thèse de Doctorat, Université Paris XI - Orsay, Décembre 1995 .

[37] Y.L Linde, A . Buzo, R .M . Gray, An algorithm for vector quantizer design , IEEE Trans. on Communications, January 1980, Vol . 28, p. 84-95 .

[38] S . Mallat, A theory for multiresolution signal decomposition : The wavelet representation, IEEE Trans. on Pattern Analysis and Machine Intelligence , July 1989, Vol. 11, N°7, p . 674-693 .

[39] A. Manduca, A . Said, Wavelet compression of medical images with set partitioning in hierarchical trees, in Proc . SPIE Medical Imaging, Newport Beach, February 1996, Vol. 2707, p . 192-200 .

[40] C.E . Metz, Some practical issues of experimental design and data analysis in radiological ROC studies, Investigative Radiology, 1989, Vol. 24, p . 234-245 .

[41] V. Nzomingy, Compression sans pertes de séquences d'images biomédicales , Thèse de Doctorat, Université de Rennes 1, Décembre 1995, 214p.

[42] K. O'Malley, K .G . Davenport, Implementation of a stand-alone optical archive : The benefits of PACS without the problems, in Proc. SPIE Medical Imaging, Newport Beach, February 1994, Vol . 2165, p . 78-85 .

[43] W.B . Pennebaker, J.L. Mitchell, JPEG still image data compression standard , New York : Van Nostrand, 1993 .

[44] PIOPED Investigators, Value of the ventilation/perfusion scan in acute pulmonary embolism : results of the prospective investigation of pulmonary embolism diagnosis (PIOPED), JAMA, 1990, Vol . 263, p . 2753-2759 .

[45] G . Poggi, R.A . Olshen, Pruned tree structured vector quantization of medical images with segmentation and improved prediction, IEEE Trans. on Image Processing, June 1995, Vol . 4, N°6, p . 734-742 .

[46] M. Rabbani, P.W. Jones, Digital image compression techniques, Washington : SPIE Optical Engineering Press, 1991, 221 p .

[47] K. Ramchandran, M . Vetterli, Best wavelet packet bases in a rate-distortion sense, IEEE Trans. on Image Processing, April 1993, Vol . 2, N°2, p . 160-175 .

[48] M. Remy-Jardin, J . Remy, F. Deschilde, D . Artaud, J .P. Bergei, C . Hossein - Foucher, X . Marchandise, A . Duhamel, Diagnosis of pulmonary embolism with spiral CT : comparison with pulmonary angiography and scintigraphy , Radiology, 1996, Vol . 200, p . 699-706 .

[49] O . Rioul, P. Duhamel, Fast algorithms for discrete and continuous wavelet transforms, IEEE Trans . on Information Theory, March 1992, Vol .38, N°2 , p . 569-586 .

[50] A . Said, WA . Pearlman, A new fast and efficient image codec based on set partitioning in hierarchical trees, IEEE Trans . on Circuits and Systems for Video Technologies, June 1996, Vol. 6 .

[51] J. Sayre, D .R . Aberle, I . Boechat, T.R . Hall, H .K . Huang, B .K. Ho ,Effect of data compression on diagnostic accuracy in digital hand and chest radiography, in Proc . SPIE Medical Imaging, Newport Beach, February 1992 , Vol . 1653, p . 233-240.