Edge Detection of Femur Bones in X-ray images - A comparative study of Edge Detectors

images carry huge amount of information for the analysis of various diseases in the human body. The X-ray images are used for examining bone structure and other tissues. Also, the clear conclusion about disease diagnosis and treatment can be drafted out from the medical experts based on the X-ray images. The objective of this paper is to compare the performance of edge detectors used for edge detection of the human femur bone in X-ray images. The experimentation has been done with various edge detectors, namely, Roberts, Sobel, Prewitt, Canny's and Laplace operators. The results show that the Laplace operator performs better than other methods in its application to X-ray images of femur bones, which has significance to medical and forensic experts.

[1]  R. B. Paris,et al.  The discrete analogue of Laplace's method , 2011, Comput. Math. Appl..

[2]  Allan Hanbury,et al.  Proceedings of the 12th international conference on Computer analysis of images and patterns , 1993 .

[3]  Jian Liu,et al.  Novel 3D Reconstruction Modeling Contributes to Development of Orthopaedic Surgical Interventions , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[4]  Feng Ding,et al.  Automatic Segmentation of Femur Bones in Anterior-Posterior Pelvis X-Ray Images , 2007, CAIP.

[5]  William K. Pratt,et al.  Digital Image Processing: PIKS Inside , 2001 .

[6]  L. J. Kitchen,et al.  The effect of spatial discretization on the magnitude and direction response of simple differential edge operators on a step edge , 1987, Comput. Vis. Graph. Image Process..

[7]  N Loveridge,et al.  Super‐osteons (remodeling clusters) in the cortex of the femoral shaft: Influence of age and gender , 2001, The Anatomical record.

[8]  Zhao Yong,et al.  An Adaptive Edge-Detection Method Based on the Canny Operator , 2009, ESIAT.

[9]  Nabeel Tahir,et al.  INTERNATIONAL JOURNAL OF IMAGE PROCESSING (IJIP) , 2012 .

[11]  J. Patel,et al.  Fuzzy inference based edge detection system using Sobel and Laplacian of Gaussian operators , 2011, ICWET.

[12]  Jim Graham,et al.  Multi-scale rigid registration to detect damage in micro-CT images of progressively loaded bones , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[13]  J. C. Stewien,et al.  The asterisk operator. An edge detection operator addressing the problem of clean edges in bone X-ray images , 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

[14]  Stephen Balter.  State of the Art of Conventional X-Ray Equipment , 1980, IEEE Transactions on Nuclear Science.

[15]  Christian Krettek,et al.  A standardized fracture reduction model for long bones--implication and evaluation in the femur. , 2010, Technology and health care : official journal of the European Society for Engineering and Medicine.

[16]  Kosin Chamnongthai,et al.  Noninvasive Femur Bone Volume Estimation Based on X-Ray Attenuation of a Single Radiographic Image and Medical Knowledge , 2008, IEICE Trans. Inf. Syst..

[17]  Dan Dragomir-Daescu,et al.  Validated finite element models of the proximal femur using two-dimensional projected geometry and bone density , 2011, Comput. Methods Programs Biomed..

[18]  Yukun Cao,et al.  Notice of RetractionApple Image Classification Method Based on the Prewitt Operator , 2009, 2009 First International Conference on Information Science and Engineering.

[19]  R. Maini.  Study and Comparison of Various Image Edge Detection Techniques , 2004 .