Image Analysis for Obturator Foramen Based on Marker-Controlled Watershed Segmentation and Zernike Moments
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:71316", author = "Seda Sahin and Emin Akata", title = "Image Analysis for Obturator Foramen Based on Marker-Controlled Watershed Segmentation and Zernike Moments", 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.", volume = "9", number = "10", pages = "2217-7", }