Image Analysis for Obturator Foramen Based on Marker-Controlled Watershed Segmentation and Zernike Moments

Authors: Seda Sahin, Emin Akata

Abstract:

Obturator Foramen is a specific structure in Pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as Obturator Foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template as a preprocessing step for computation of Pelvic bone rotation on hip radiographs. This method consists of integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor and it is used to detect Obturator Foramen accurately. Marker-controlled Watershed segmentation is applied to separate Obturator Foramen from the background effectively. Then, Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for final extraction of Obturator Foramens. Finally, Pelvic bone rotation rate calculation for each hip radiograph is performed automatically to select and eliminate hip radiographs for further studies which depend on Pelvic bone angle measurements. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results demonstrated that the proposed method is able to segment Obturator Foramen with 96% accuracy.

Keywords: Medical image analysis, marker-controlled watershed segmentation, segmentation of bone structures on hip radiographs, pelvic bone rotation rate, zernike moment feature descriptor.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109872

References:

[1] F. G. Boniforti, G. Fujii, R. D. Angliss, M. K. D. Benson, “The reliability of measurements of Pelvic Radiographs in infants”, J Bone Joint Surg (Br), vol. 79-B, no. 4, pp. 570-575, 1997.
[2] D. Tönnis , “Normal values of the hip joint for the evaluation of X-rays in children and adults”, Clinical Orthopaedics, vol. 119, pp. 39-47, 1976.
[3] I. N. Bankman, Handbook of Medical Imaging, Academic Press, 2000.
[4] R. C. Gonzalez, R. E. Woods, Digital İmage Processing, Second Edition, Prentice Hall, 2002.
[5] X. Zhang, F. Jia, S. Luo, G. Liu, Q. Hu, “A marker-based watershed method for X-ray image segmentation”, Computer Methods And Programs in Biomedicine, vol. 113, pp. 894-903, 2014.
[6] S.S. Kumar, R.S. Moni, J. Rajeesh, “Automatic Segmentation of Liver and Tumor for CAD of Liver”, Journal of Advances in Information Technology, vol. 2, issue1, 2011.
[7] J. Mehena, M. C. Adhikary, “Brain Tumor Segmentation and Extraction of MR Images Based on Improved Watershed Transform”, IOSR Journal of Computer Engineering, vol. 17, issue 1, pp. 1-5, 2015.
[8] A. W. Reza, C. Eswaran, K. Dimyati, “Diagnosis of Diabetic Retinopathy: Automatic Extraction of Optic Disc and Exudates from Retinal Images using Marker-controlled Watershed Transformation”, J Med Syst, vol. 35, pp. 1491-1501, 2011.
[9] S. W. Foo, Q. Dong, “A Feature-based Invariant Watermarking Scheme Using Zernike Moments”, World Academy of Science, Engineering and Technology, vol. 4, 2010.
[10] A. Tahmasbi, F. Saki, S. B. Shokouhi, “Classification of benign and malignant masses based on Zernike moments”, Computers in Biology and Medicine, vol. 41, pp. 726-735, 2011.
[11] F. Saki, A. Tahmasbi, H. Soltanian-Zadeh, S. B:. Shokouhi, “Fast opposite weight learning rules with application in breast cancer diagnosis”, Computers in Biology and Medicine, vol. xx, pp., 2012.
[12] M. Zhenjiang, “Zernike moment-based image shape analysis and its application”, Pattern Recognition Letters, vol. 21, pp. 169-177, 2000.
[13] S. Sharma, P. Khanna, “Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM”, J Digit Imaging, vol.28, pp. 77-90, 2015.
[14] A. E. Villafuerte-Nuñez, A. C. Téllez-Anguiano, O. Hernández-Díaz, R. Rodríguez-Vera, J. A. Gutiérrez-Gnecchi, J. L. Salazar-Martínez, “Facial Edema Evaluation Using Digital Image Processing”, Hindawi Publishing Corporation, Discrete Dynamics in Nature and Society, Volume 2013, Article ID 927843, 2013.
[15] T. Fawcett, “An introduction to ROC Analysis”, Pattern Recognition Letters, vol. 27, pp. 861-874, 2006.
[16] J. Bozek, M. Mustra, K. Delac, M. Grgic, “A Survey of Image Processing Algorithms in Digital Mammography”, Rec.Advan. in Mult. Sig. Process. and Commun., SCI 231, pp. 631–657, 2009.