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Comparative Study of Segmentation Techniques for Basal Ganglia Detection Based on Positron Emission Tomography Images

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Abstract

The Basal Ganglia (BG) is a small brain structure that plays a significant role in the Parkinson’s disease (PD) pathogenesis. Since it provides detailed quantitative information for the PD progression, positron emission tomography (PET) is an ideal tool for the detection of the amount changes in this region. To make these images easy to analyze and more meaningful, it is crucial to modify their representation and clarify the image through segmentation. It is critical to locate the BG region so that it can be easily examined to detect its size and shape for progression diagnosis. In this paper, an exhaustive study of BG segmentation from PET images is performed through different segmentation techniques, namely binary thresholding, truncate thresholding, threshold to zero, Otsu thresholding, and clustering combined with binary thresholding. These techniques have been tested onto 110 PET images and evaluated with their corresponding ground truth which were previously segmented manually. We evaluated the segmentation performance using two metrics Dice Similarity Coefficient (DSC) and mean Intersection over Union (mIoU). The obtained results indicate that binary thresholding technique outperforms other segmentation techniques and reached higher performance using 150 as threshold with DSC of 0.7701 and mIoU of 0.6394.

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Data availability

The data used in this paper was obtained from the Parkinson’s Progressive Marker Initiative (PPMI) database (https://www.ppmi-info.org/). These restrictions must be applied: Investigators seeking access to the PPMI database are asked to submit an online application then sign the data use agreement and conform with the study publications policy. Requests for accessing this database should be oriented to the PPMI Data and Publications Committee (DPC) (via: https://www.ppmi-info.org/access-data-specimens/download-data).

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Authors and Affiliations

  1. Management Computing Department, Institut Supérieur de Gestion de Tunis, 41 Avenue de la Liberté, 2000, Tunis, Tunisia

    Zainab Maalej, Fahmi Ben Rejab & Kaouther Nouira

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  1. Zainab Maalej
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  2. Fahmi Ben Rejab
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  3. Kaouther Nouira
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All the authors contributed to the study conception, design, and paper selection. The first draft of the paper was written by ZM and all the authors commented on previous versions of the article. FBR and KN revised the work, and have approved the submitted version.

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Maalej, Z., Ben Rejab, F. & Nouira, K. Comparative Study of Segmentation Techniques for Basal Ganglia Detection Based on Positron Emission Tomography Images. SN COMPUT. SCI. 5, 364 (2024). https://doi.org/10.1007/s42979-024-02677-9

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