Edge Detection Algorithm Based on BEMD for Liver CT Images
Gajendra Kumar Mourya1, Manashjit Gogoi2, Akash Handique3
1Gajendra Kumar Mourya*, Department of Biomedical Engineering, School of Technology, North-Eastern Hill University, Shillong, Meghalaya, India.
2Dr. Manashjit Gogoi, Department of Biomedical Engineering, School of Technology, North-Eastern Hill University, Shillong, Meghalaya, India.
3Dr. Akash Handique, Department of Radiology & Imaging, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, Meghalaya, India.
Manuscript received on December 12, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 2090-2094 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8930019320/2020©BEIESP | DOI: 10.35940/ijitee.C8930.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Liver edge identification requires for its volume estimation from CT image and this process is a prerequisite for liver diagnosis and treatment planning. In this article, an edge detection algorithm proposed based on Bi-dimensional Empirical Mode Decomposition (BEMD) and Fourier Transform. Intrinsic mode function (IMF) extracted from BEMD and mixed with the Fourier phase of the original image to get edge profile. The proposed method extensively evaluated on Berkeley Segmentation Data Set (BSDS-500) and compared with Sobel and Canny operators. Results achieved Mean Square error 0.04±0.01 and PSNR 62.27±1.1. In conclusion, The BEMD approach capable of identifying image edges with high accuracy compared with state of the art.
Keywords: BEMD, CT Image, Edge Detection, Fourier Transform, IMF
Scope of the Article: Image Analysis and Processing