ICTACT Journal on Image and Video Processing

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Extending Benefit Based Segmentation Techniques Performance Analysis Over Intensity Non Uniformed Brain MR Images


Affiliations
1 Post Graduate and Research Department of Computer Science, Sadakathullah Appa College, India
2 Post Graduate and Research Department of Computer Science, Sadakathullah Appa College
     

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The bias field is an undesirable image foible that formulate during the process of image procurement. Segmentation is the procedure of segregating a digital image into constituent component or substantial segments which help in extracting quality amount of information from the region of interest. There is several bias correction strategies have been recommended till date, all these algorithms helps in reducing bias but none of them perfectly removes bias. When incorporating computer aided diagnosing in treatment planning, the leftover bias cause to inaccurate segmentation which leads to faulty diagnosis of the diseases. This paper scrutinizes the segmentation algorithms over bias corrupted brain MR Images and analyzes which segmentation algorithm efficiently segments the image components even though it is corrupted by bias field. The bench mark brain MR Images with different bias spectrum is employed for the research. Quantitative metrics are adopted to conclude the result. The outcome of this paper tends to provide accuracy in computer aided diagnosing and to elect appropriate segmentation technique while developing bias correction based segmentation algorithm.

Keywords

Bias Field, Chan Vese, Expectation Maximization, Fuzzy Level Set, Distance Regularized Level Set Evaluation.
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  • Nida M. Zaitoun and Musbah J. Aqel, “Survey on Image Segmentation Techniques”, Proceedings of International Conference on Communication, Management and Information Technology, pp. 797-806, 2015.

  • Neeraj Sharma and Lalit M. Aggarwal, “Automated Medical Image Segmentation Techniques”, Journal of Medical Physics, Vol. 35, No. 1, pp. 1-14, 2010.

  • G.H. Glover, C.E. Hayes and N.J. Pelc, “Comparison of Linear and Circular Polarization for Magnetic Resonance Imaging”, Journal of Magnetic Resonance, Vol. 64, No. 2, pp. 255-270, 1985.

  • I. Harvey, P.S. Tofts, J.K. Morris, D.A.G. Wicks and M.A. Ron, “Sources of T1 Variance in Normal Human White Matter”, Magnetic Resonance Imaging, Vol. 9, No. 1, pp. 53-59, 1991.

  • A. Simmons, P.S. Tofts, G.J. Barker and S.R. Arridge, “Sources of Intensity Nonuniformity in Spin Echo Images at 1.5T,” Magnetic Resonance in Medicine, Vol. 32, No. 1, pp. 121-128, 1994.

  • G.J. Barker, A. Simmons, S.R. Arridge and P.S. Tofts, “A Simple Method for Investigating the Effects of Non-Uniformity of Radiofrequency Transmission and Radiofrequency Reception in MRI”, British Journal of Radiology, Vol. 71, No. 841, pp. 9-67, 1998.

  • M. Alecci, C.M. Collins, M.B. Smith and P. Jezzard, “Radio Frequency Magnetic Field Mapping of a 3 Tesla Birdcage Coil: Experimental and Theoretical Dependence on Sample Properties”, Magnetic Resonance in Medicine, Vol. 46, No. 2, pp. 379-385, 2001.

  • Marco Ganzetti, Nicole Wenderoth and Dante Mantini, “Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images”, Neuroinformatics, Vol. 15, No. 5, pp. 5-21, 2016.

  • Z.P. Liang, “Principles of Magnetic Resonance Imaging: A Signal Processing Perspective”, IEEE Press, 2000.

  • E.R. McVeigh, “Phase and Sensitivity of Receiver Coils in Magnetic Resonance Imaging”, Medical Physics, Vol. 13, No. 6, pp. 806-814, 1986.

  • L. Axel, J. Costantini and J. Listerud, “Technical Note Intensity”, American Journal of Roentgenology, Vol. 148, pp. 418-420, 1987.

  • D.A.G. Wicks, G.J. Barker and P.S. Tofts, “Correction of Intensity Nonuniformity in MR Images of any Orientation”, Magnetic Resonance Imaging, Vol. 11, No. 1, pp. 183-196, 1993.

  • Zujun Hou, “A Review on MR Image Intensity Inhomogeneity Correction”, International Journal of Biomedical Imaging, Vol. 2006, pp. 1-11, 2006.

  • Uros Vovk, Franjo Pernus and Bostjan Likar, “A Review of Methods for Correction of Intensity Inhomogeneity in MRI”, IEEE Transactions on Medical Imaging, Vol. 26, No. 3, pp. 1-12, 2007.

  • John G. Sled, Alex P. Zijdenbos and Alan C. Evans, “A Nonparametric Method for Automatic Correction of Intensity Nonuniformity in MRI Data”, IEEE Transactions on Medical Imaging, Vol. 17, No. 1, pp. 1-13, 1998.

  • Nicholas J. Tustison, Brian B. Avants, Philip A. Cook, Yuanjie Zheng, Alexander Egan, Paul A. Yushkevich and James C. Gee, “N4ITK: Improved N3 Bias Correction”, IEEE Transactions on Medical Imaging, Vol. 29, No. 6, pp. 1-23, 2010.

  • Maryjo M. George, S. Kalaivani and M.S. Sudhakar, “A Non-Iterative Multi-Scale Approach for Intensity Inhomogeneity Correction in MRI”, Magnetic Resonance Imaging, Vol. 42, pp. 43-59, 2017.

  • Xiance Zhao, Haibin Xie, Wenjing Li, Guang Yang and Xu Yan, “MRI Intensity Inhomogeneity Correction Based on Similar Points”, Proceedings of International Congress on Image and Signal Processing, pp. 1-5, 2017.

  • O. Salvado, C. Hillenbrand, Shaoxiang Zhang and D.L. Wilson, “Method to Correct Intensity Inhomogeneity in MR Images for Atherosclerosis Characterization”, IEEE Transactions on Medical Imaging, Vol. 25, No. 5, pp. 111-124, 2006.

  • Maryjo M George and S. Kalaivani, “Intensity Inhomogeneity Correction and Tissue Segmentation of MR Images: A Parametric Approach”, International Journal of Pure and Applied Mathematics, Vol. 115, No. 9, pp. 409-416, 2017.

  • Lei Wang, Zhu Jianbing, Sheng Mao, Adriena Cribb, Shaocheng Zhu and Jiantao Pu,” Simultaneous Segmentation and Bias Field Estimation using Local Fitted Images”, Pattern Recognition, Vol. 74, pp. 145-155, 2018.

  • Sanping Zhou, Mengmeng Zhang, Qing Cai and Yihong Gong, “Correntropy-Based Level Set Method for Medical Image Segmentation and Bias Correction”, Neurocomputing, Vol. 234, No. 19, pp. 216-229, 2017.

  • Qiang Ling, Zhaohui Li, Qinghua Huang and Xuelong Li, “A Robust Gradient Based Algorithm to Correct Bias Fields of Brain MR Images”, IEEE Transactions on Autonomous Mental Development, Vol. 7, No. 3, pp. 1-15, 2015.

  • Hui Liua, Shanshan Liu, Dongmei Guo, Yuanjie Zheng, Pinpin Tanga and Guo Dan, “Original Intensity Preserved Inhomogeneity Correction and Segmentation for Liver Magnetic Resonance Imaging”, Biomedical Signal Processing and Control, Vol. 47, No. 1, pp. 231-239, 2019.

  • Xiao-Feng Wang, De-Shuang Huang and HuanXu, “An Efficient Local Chan-Vese Model for Image Segmentation”, Pattern Recognition, Vol. 43, No. 3, pp. 603-618, 2010.

  • Zhe Zhang and Jianhua Song, “An Adaptive Fuzzy Level Set Model With Local Spatial Information for Medical Image Segmentation and Bias Correction”, IEEE Access, Vol. 7, pp. 27322-27338, 2019.

  • C.A. Cocosco, V. Kollokian, R.K.S. Kwan and A.C. Evans, “BrainWeb: Online Interface to a 3D MRI Simulated Brain Database”, NeuroImage, Vol. 5, No. 4, pp. 20-25, 1997.

  • Y. Zhang, M. Brady and S. Smith, “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm”, IEEE Transactions on Medical Imaging, Vol. 20, No. 1, pp. 45-57, 2001.

  • T.F. Chan and L.A. Vese, “Active Contours without Edges”, IEEE Transactions on Image Processing, Vol. 10, No. 2, pp. 266-277, 2001. [30] C. Li, C. Xu and C. Gui, “Distance Regularized Level Set Evolution and Its Application to Image Segmentation”, IEEE Transactions on Image Processing, Vol. 19, No. 12, pp. 3243-3254, 2010.


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  • Extending Benefit Based Segmentation Techniques Performance Analysis Over Intensity Non Uniformed Brain MR Images

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Authors

A. Farzana
Post Graduate and Research Department of Computer Science, Sadakathullah Appa College, India
M. Mohamed Sathik
Post Graduate and Research Department of Computer Science, Sadakathullah Appa College
S. Shajun Nisha
Post Graduate and Research Department of Computer Science, Sadakathullah Appa College, India

Abstract


The bias field is an undesirable image foible that formulate during the process of image procurement. Segmentation is the procedure of segregating a digital image into constituent component or substantial segments which help in extracting quality amount of information from the region of interest. There is several bias correction strategies have been recommended till date, all these algorithms helps in reducing bias but none of them perfectly removes bias. When incorporating computer aided diagnosing in treatment planning, the leftover bias cause to inaccurate segmentation which leads to faulty diagnosis of the diseases. This paper scrutinizes the segmentation algorithms over bias corrupted brain MR Images and analyzes which segmentation algorithm efficiently segments the image components even though it is corrupted by bias field. The bench mark brain MR Images with different bias spectrum is employed for the research. Quantitative metrics are adopted to conclude the result. The outcome of this paper tends to provide accuracy in computer aided diagnosing and to elect appropriate segmentation technique while developing bias correction based segmentation algorithm.

Keywords


Bias Field, Chan Vese, Expectation Maximization, Fuzzy Level Set, Distance Regularized Level Set Evaluation.

References