Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Impact of region of interest size on transcranial sonography based computer-aided diagnosis for Parkinson’s disease

1 Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
2 Department of Ultrasonography, Shanghai East Hospital of Tongji University, Shanghai, China
3 Department of Neurology, Shanghai East Hospital of Tongji University, Shanghai, China
4 Department of Ultrasound, The Second Affiliated Hospital of Soochow University, China

Special Issues: Advanced Computer Methods and Programs in Biomedicine

Transcranial sonography (TCS) has gained increasing application for diagnosis of Parkinson’s disease (PD) in clinical practice in recent years, because most PD patients, even in the early stage of PD, have abnormal hyperechogenicity of the substantia nigra (SN) in brainstem shown in TCS images. Therefore, the region of interest (ROI) for feature extraction should cover the SN region in a computer-aided diagnosis (CAD) system. The ROI size naturally affects the feature representation. However, there currently exist no unified standard for determining the size of ROI. In this work, we quantitatively compare the performance of TCS-based CAD with three sizes of ROIs, namely the entire midbrain (EM) region, the half of midbrain (HoM) region and the SN region. The experimental results on the original extracted features and the features by dimensionality reduction show that ROI covering the EM region achieves the overall best diagnosis performance. The results indicates that the neighboring regions around SN might also have abnormal symptoms, which cannot be clearly observed with naked eyes. It suggests that the large ROI includes more information for feature representation to improve the diagnosis performance of TCS-based CAD for PD.
  Figure/Table
  Supplementary
  Article Metrics

Keywords Parkinson’s disease; transcranial sonography; region of interest; substantia nigra

Citation: Xiaoyan Fei, Yun Dong, Hedi An, Qi Zhang, Yingchun Zhang, Jun Shi. Impact of region of interest size on transcranial sonography based computer-aided diagnosis for Parkinson’s disease. Mathematical Biosciences and Engineering, 2019, 16(5): 5640-5651. doi: 10.3934/mbe.2019280

References

  • 1. R. E. Burke and O. K. Malley, Axon degeneration in Parkinson's disease. Exp. Neurol., 246(2013), 72–83.
  • 2. C. Weingarten, M. Sundman, P. Hickey, et al., Neuroimaging of Parkinson's disease: Expanding views, Neurosci. Biobehav. Rev., 59(2015), 16–52.
  • 3. D. Frosini, M. Cosottini, D. Volterrani, et al., Neuroimaging in Parkinson's disease: Focus on substantia nigra and nigro-striatal projection. Curr. Opin. Neurol., 30(2017), 416–426.
  • 4. D. Berg, Ultrasound in the (premotor ) diagnosis of Parkinson ' s disease, Park. Relat. Disord., 13(2007),13.
  • 5. Q. Huang, F. Zhang and X. Li, Machine learning in ultrasound computer-aided diagnostic systems: A survey, BioMed. Res. Int., 2018, Article ID: 5137904.
  • 6. S. Zhou, J. Shi, J. Zhu, et al., Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image, Biomed. Signal Process. Control, 8(2013), 688–696.
  • 7. G. Lehang, D. Wang, Y. Qian, et al., A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images, Clin. Hemorheol. Microcirc., 69(2018), 343–354.
  • 8. Q. Huang, Y. Chen, L. Liu, et al., On combining biclustering mining and AdaBoost for breast tumor classification, IEEE Trans. Knowl. Data Eng., 2019, in press. DOI: 10.1109/TKDE.2019.2891622
  • 9. L. Chen, J. Hagenah and A. Mertins, Feature analysis for Parkinson's disease detection based on transcranial sonography image, In the 15th International Conference on Medical Image Computing and Computer-Assited Intervention, 2012, 272–279.
  • 10. O. Pauly, S. Ahmadi, A. Plate, et al., Detection of substantia nigra echogenicities in 3D transcranial ultrasound for early diagnosis of Parkinson disease, In the 15th International Conference on Medical Image Computing and Computer-Assited Intervention, 2012, 443–450.
  • 11. A. Plate, S. Ahmadi, O. Pauly, et al., Three-dimensional sonographic examination of the midbrain for computer-aided diagnosis of movement disorders, Ultrasound Med. Biol., 38(2012), 2041–2050.
  • 12. A. Sakalauskas, K. Laučkaitė, A. Lukoševičius, et al., Computer-aided segmentation of the mid-brain in trans-cranial ultrasound images, Ultrasound Med. Biol., 42(2016), 322–332.
  • 13. A. Sakalauskas, V. Špečkauskienė, K. Laučkaitė, et al., A. Lukoševičius, Transcranial ultrasonographic image analysis system for decision support in Parkinson disease, J. Ultrasound Med., 2018.
  • 14. B. Gong, J. Shi, S. Ying, et al., Neuroimaging-based diagnosis of Parkinson's disease with deep neural mapping large margin distribution machine, Neurocomputing, 320(2018), 141–149.
  • 15. J. Shi, Z. Y. Xue, Y. K. Dai, et al., Cascaded multi-column RVFL+ classifier for single-modal neuroimaging-based diagnosis of Parkinson's disease, IEEE Trans. Biomed. Eng., 2019, in press. DOI: 10.1109/TBME.2018.2889398.
  • 16. K. Skerl, S. Vinnicombe, E. Giannotti, et al., Influence of region of interest size and ultrasound lesion size on the performance of 2D shear wave elastography (SWE) in solid breast masses, Clin. Radiol., 70 (2015) 1421–1427.
  • 17. J. Moon, J. Hwang, J. Park, et al., Impact of region of interest (ROI) size on the diagnostic performance of shear wave elastography in differentiating solid breast lesions, Acta Radiol., 59 (2018), 657–663.
  • 18. Q. Zhang, Y. Xiao, J. Suo, et al., Sonoelastomics for breast tumor classification: a radiomics approach with clustering-based feature selection on sonoelastography, Ultrasound Med. Biol., 43 (2017), 1058–1069.
  • 19. Y. Ma and L. Zhu, A review on dimension reduction, Int. Statist. Rev., 81(2013), 134–150.
  • 20. J. Shi, Q. Jiang, Q. Zhang, et al., Sparse kernel entropy component analysis for dimensionality reduction of biomedical data, Neurocomputing, 168 (2015), 930–940.
  • 21. J. Shi, Q. Jiang, R. Mao, et al., FR-KECA: Fuzzy robust kernel entropy component analysis, Neurocomputing, 149 (2015), 1415–1423.
  • 22. B. Mwangi, T. Tian and J. Soares, A review of feature reduction techniques in neuroimaging, Neuroinformatics, 12 (2014), 229–244.
  • 23. C. Ding and H. Peng, Minimum redundancy feature selection from microarray gene expression data, J. Bioinform. Comput. Biol., 3(2005), 185–205.

 

Reader Comments

your name: *   your email: *  

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved