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A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation

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Abstract

In this paper, we propose a new collaborative process that aims to detect macrocalcifications from mammographic images while minimizing false negative detections. This process is made up of three main phases: suspicious area detection, candidate object identification, and collaborative classification. The main concept is to operate on the entire image divided into homogenous regions called superpixels which are used to identify both suspicious areas and candidate objects. The collaborative classification phase consists in making the initial results of different microcalcification detectors collaborate in order to produce a new common decision and reduce their initial disagreements. The detectors share the information about their detected objects and associated labels in order to refine their initial decisions based on those of the other collaborators. This refinement consists of iteratively updating the candidate object labels of each detector following local and contextual analyses based on prior knowledge about the links between super pixels and macrocalcifications. This process iteratively reduces the disagreement between different detectors and estimates local reliability terms for each super pixel. The final result is obtained by a conjunctive combination of the new detector decisions reached by the collaborative process. The proposed approach is evaluated on the publicly available INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to existing detectors as well as ordinary fusion operators.

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Authors and Affiliations

  1. Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia

    Asma Touil, Karim Kalti & Mohamed Ali Mahjoub

  2. Université de Sousse, Supérieur d’Informatique et des Techniques de Communication, Hammam Sousse, 4011, Tunisia

    Asma Touil, Karim Kalti & Mohamed Ali Mahjoub

  3. IMT Atlantique, LaTIM UMR 1101, Brest, 29200, France

    Asma Touil, Pierre-Henri Conze & Basel Solaiman

  4. University of Monastir, Computer Science Department, Faculty of Science, 5019, Monastir, Tunisia

    Karim Kalti

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  1. Asma Touil
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  2. Karim Kalti
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  3. Pierre-Henri Conze
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  4. Basel Solaiman
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  5. Mohamed Ali Mahjoub
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Correspondence to Asma Touil.

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Touil, A., Kalti, K., Conze, PH. et al. A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation. J Digit Imaging 35, 1560–1575 (2022). https://doi.org/10.1007/s10278-022-00678-9

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  • DOI: https://doi.org/10.1007/s10278-022-00678-9

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