Object-oriented classification approach for bone metastasis mapping from whole-body bone scintigraphy

  • Author Footnotes
    1 ORCID: 0000-0002-6135-7850.
    Mihaela Antonina Calin
    Corresponding author at: National Institute of Research and Development for Optoelectronics INOE 2000, 409 Atomistilor Street, P.O. BOX MG5, Magurele, Ilfov 077125, Romania.
    1 ORCID: 0000-0002-6135-7850.
    National Institute of Research and Development for Optoelectronics - INOE 2000, Magurele, Romania
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  • Florina-Gianina Elfarra
    “Saint John” Emergency Clinical Hospital, Bucharest, Romania

    University of Bucharest, Faculty of Physics, Magurele, Romania
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  • Author Footnotes
    2 ORCID: 0000-0003-3986-0385.
    Sorin Viorel Parasca
    2 ORCID: 0000-0003-3986-0385.
    Carol Davila” University of Medicine and Pharmacy, Bucharest, Romania
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  • Author Footnotes
    1 ORCID: 0000-0002-6135-7850.
    2 ORCID: 0000-0003-3986-0385.
Published:April 21, 2021DOI:


      • An edge-based segmentation approach can identify objects in whole body bone scintigraphy.
      • The objects have specific textural and spatial attributes.
      • K-nearest-neighbor and support vector machine can classify previously identified objects.
      • An error matrix was used to evaluate the method’s performance.
      • The object - based classification method can detect and map metastases in whole body scintigraphy.



      Whole-body bone scintigraphy is the most widely used method for detecting bone metastases in advanced cancer. However, its interpretation depends on the experience of the radiologist. Some automatic interpretation systems have been developed in order to improve diagnostic accuracy. These systems are pixel-based and do not use spatial or textural information of groups of pixels, which could be very important for classifying images with better accuracy. This paper presents a fast method of object-oriented classification that facilitates easier interpretation of bone scintigraphy images.


      Nine whole-body images from patients suspected with bone metastases were analyzed in this preliminary study. First, an edge-based segmentation algorithm together with the full lambda-schedule algorithm were used to identify the object in the bone scintigraphy and the textural and spatial attributes of these objects were calculated. Then, a set of objects (224 objects, ~ 46% of the total objects) were selected as training data based on visual examination of the image, and were assigned to various levels of radionuclide accumulation before performing the data classification using both k-nearest-neighbor and support vector machine classifiers. The performance of the proposed method was evaluated using as metric the statistical parameters calculated from error matrix.


      The results revealed that the proposed object-oriented classification approach using either k-nearest-neighbor or support vector machine as classification methods performed well in detecting bone metastasis in terms of overall accuracy (86.62 ± 2.163% and 86.81 ± 2.137% respectively) and kappa coefficient (0.6395 ± 0.0143 and 0.6481 ± 0.0218 respectively).


      In conclusion, the described method provided encouraging results in mapping bone metastases in whole-body bone scintigraphy.

      Graphical abstract


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