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Joint variational segmentation of CT/PET data using non-local active contours and belief functions

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Abstract

In this paper, we have proposed a new framework to use both PET and CT images simultaneously for tumor segmentation. Our method combines the strength of each imaging modality: the superior contrast of PET and the superior spatial resolution of CT. We formulate this problem as a Non-Local Active Contours (NL-AC) based-variational segmentation framework incorporating Belief Functions (BFs). The proposed method used all features issued from both modalities (CT and PET) as a descriptor to drive the NL-AC curve evolution. The new segmentation framework allows us to incorporate in the same framework heterogeneous knowledge in order to reduce the imprecision due to noise poor contrast, weak or missing boundaries of objects, inhomogeneities, etc. The proposed method was evaluated on relevant tumor segmentation problems. The results showed that our method can effectively make use of both PET and CT image information, yielding segmentation accuracy of 81.52% in Dice Similarity Coefficient (DSC) and the Average Symmetric Surface Distance (ASSD) of 1.2 ± 0.8 mm, which is 10% (resp., 16%) improvement compared to two state of art segmentation methods using the PET (resp., CT) images.

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Authors and Affiliations

  1. Univ Nord de France, F-59000, Lille, France

    F. Derraz, A. Pinti & L. Peyrodie

  2. UCL, Faculté de médecine et maieutique, Paris, 59000, France

    F. Derraz

  3. Univ. Abou Bekr Belkaid, Tlemcen, Telecommunication Laboratory of Tlemcen, Technology Faculty, Algeria, France

    F. Derraz & M. Bousahla

  4. ENSIAME, UVHC, 59300, Valenciennes, France

    A. Pinti

  5. Hautes Etudes d’Ingénieur, LAGIS UMR 8219 CNRS, Paris, France

    L. Peyrodie

  6. EA 4708, I3MTO, CHRO, 1 rue Porte Madeleine, 45032, Orléans, France

    A. Pinti & H. Toumi

Authors
  1. F. Derraz
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  2. A. Pinti
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  4. M. Bousahla
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  5. H. Toumi
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Correspondence to F. Derraz.

Additional information

This paper uses the materials of the report submitted at the 11th International Conference “Pattern Recognition and Image Analysis: New Information Technologies,” Samara, Russia, September 23–28, 2013.

The article is published in the original.

Foued Derraz was born in Tlemcen, Algeria, in 1970. He received the Ph.D. degree in Computer science, image and signal processing from Valenciennes university, France, in 2010. His current scientific interests include variational methods in image analysis, multimodal signal processing, medical image analysis, including multi-modal image registration, segmentation, computer-assisted surgery, and diffusion MRI.

Antonio Pinti was born in France, in 1965. He received his PhD in Computer Sciences and biomedical engineering from the University of Mulhouse in 1993 (France), and is also assigned to the I3MTO laboratory at the University of Orleans (France). He is currently assistant professor at the University of Valenciennes (France). His research interests include modeling, image and signal processing for biomechanics applied to sports and rehabilitation.

Laurent Peyrodie received his PhD in automation from University of Sciences and technics of Lille in 1996, France. He is currently Associate professor at department of Energy Electrical and automation of Ecole des Hautes Etudes d’Ingenieur Lille. His research focuses on biomedical signal and image processing. His current scientific interests include EEG signal filtering, epilepy seizure detection.

Miloud Bousahla was born in sidi-BelAbess, Algeria,in 1969. He received the Elect. Eng. degree from Sidi Bel Abbes University in 1993, the magister in signal and systems, and Ph.D. degree in electronics from Tlemcen University, Algeria, in 1999 and 2012 respectively. His research interests include computational electromagnetics, microwave imaging and antennas for biological and medical applications.

Hechmi Toumi was born in Tunisia, 1971. He received PhD degree from Blaise Pascal University, France. Researcher assistant at Wisconsin University and awarded professor at the University of Wales, UK. He is a Member of the Editorial boards the journal Medicine and Science in Sports and Exercise and Journal of Foot and Ankle.

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Derraz, F., Pinti, A., Peyrodie, L. et al. Joint variational segmentation of CT/PET data using non-local active contours and belief functions. Pattern Recognit. Image Anal. 25, 407–412 (2015). https://doi.org/10.1134/S1054661815030049

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  • DOI: https://doi.org/10.1134/S1054661815030049

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