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Low-contrast lesion segmentation in advanced MRI experiments by time-domain Ricker-type wavelets and fuzzy 2-means

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Abstract

Automated suspicious region segmentation has become a crucial need for the experts dealing with numerous images containing contrast-based lesions in MRI. Not all solutions, however, are based on mathematical infrastructure or providing adequate flexibility. On the other hand, segmentation of low-contrast lesions is very challenging for researchers; therefore, advanced magnetic resonance imaging (MRI) experiments are not commonly used in researches. Given the need of repeatability and adaptability, we present an automated framework for intelligent segmentation of brain lesions by wavelet imaging and fuzzy 2-means. Besides the general use of the wavelets in image processing, which is edge detection; we employed the second-order Ricker-type wavelets as the core of our novel imaging framework for low-contrast lesion segmentation. We firstly introduced the mathematical basis of several Ricker wavelet functions, which are in symmetrical form satisfying finite-energy and admissibility conditions of mother wavelets. Afterwards, we investigated three types of Ricker wavelets to apply on our clinical dataset containing susceptibility-weighted (SW) and minimum intensity projection SW (mIP-SW) images with barely-visible lesions. Finally, we adjusted the system parameters of the wavelets for optimization and post-segmentation by fuzzy 2-means. According to the preliminary results of the clinical experiments we conducted, our framework provided 93.53% average dice score (DSC) for SWI by Ricker-3 and 92.56% for mIP-SWI by Ricker-2 wavelet, as the main performance criteria of segmentation. Despite the lack of SWI or mIP-SWI experiments in the public datasets, we tested our framework with BraTS 2012 training sets containing real images with visible lesions and achieved an average of 88.13% DSC with 11.66% standard deviation by re-optimized framework for whole lesion segmentation, which is one of the highest among other relevant researches. In detail, 87.52% DSC for LG datasets with 11.32% standard deviation; while 88.34% DSC for HG datasets with 11.77% standard deviation are calculated.

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Acknowledgements

The authors of the article are grateful to the Clinic of Neurosurgery, University Hospital of Hradec Kralove, for providing MRI sequence of infiltrative tumor case under IT4Neuro project. The study conforms to the rules of University Hospital Ethical Board and it was anonymized accordingly so as to protect the privacy and dignity of any involved people. The work was funded by the project IT4Neuro (degeneration), reg. nr. CZ.02.1.01/0.0/0.0/ 18_069/0010054, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic.

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Authors and Affiliations

  1. Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 50003, Hradec Kralove, Czech Republic

    Orcan Alpar & Ondrej Krejcar

  2. Biomedical Research Center, University Hospital Hradec Kralove, Sokolska 581, 50005, Hradec Kralove, Czech Republic

    Rafael Dolezal

  3. Department of Diagnostic Radiology, Charles University Faculty of Medicine in Hradec Kralove, University Hospital Hradec Kralove, Sokolska 581, 50005, Hradec Kralove, Czech Republic

    Pavel Ryska

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  1. Orcan Alpar
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Correspondence to Orcan Alpar or Ondrej Krejcar.

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Alpar, O., Dolezal, R., Ryska, P. et al. Low-contrast lesion segmentation in advanced MRI experiments by time-domain Ricker-type wavelets and fuzzy 2-means. Appl Intell 52, 15237–15258 (2022). https://doi.org/10.1007/s10489-022-03184-1

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