An Image Segmentation Algorithm for Gradient Target Based on Mean-Shift and Dictionary Learning
In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132637Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 471
 Pablo Arbelaez, Michael Maire, Charless Fowlkes. Contour Detection and Hierarchical Image Segmentation (J). IEEE Transactions on pattern analysis and machine intelligence. 2011, Vol.33, No.5, pp:898-916.
 A. D. Brink, Thresholding of digital images using two-dimensional entropies (J), Pattern Recognition, 1992, 25, 803-808.
 Zhang Yongmei, Ba Dekai, Xing Kuo. A Method of Fuzzy Threshold for Adaptive Image Segmentation (J). Computer Measurement & Control. 2016, 24(4), pp:126-128,136.
 Gang Yuan. Research on multiband image fusion algorithm based on clustering and multiscale decomposition (D). University of Electronic Science and Technology of China. 2015.
 Dorin Comaniciu, Peter Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis (J). IEEE Transactions on pattern analysis and machine intelligence. 2002, Vol.24, No.5, pp:898-916.
 Michael Elad, Michal Ahoron. Image Denoising Via Learned Dictionaries and Sparse representation (C). 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR’06).
 Wenbing Tao, Hai Jin, Yimin Zhang. Color Image Segmentation Based on Mean Shift and Normalized Cuts (J). IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetics. 2007, Vol.37, No.5, pp:1382-1388.
 Xiong Ping, Bai Yunpeng. Mean Shift image segmentation algorithm with adaptive bandwidth (J). Computer Engineering and Applications, 2013, 49(23):174-176.
 Zhou Jiaxiang, Zhu Jianjun, Mei Xiaoming, Ma Huiyun. An Adaptive MeanShift Segmentation Method of Remote Sensing Images Based on Multi-Dimension Features (J). Geomatics and Information Science of Wuhan University. 2012, Vol.37, No.4, pp:419-422,440.
 Chao Lai, Fangzhao Li, Bao Li, Shiyao Jin. Image Super-Resolution Based on Segmentation and Classification with sparsity (C). 2016 2nd IEEE International Conference on Computer and Communications.
 Zhengdong Zhang, Arvind Ganesh, Xiao Liang. TILT: Transform Invariant Low-Rank Textures (C). Int J Comput Vis (2012) 99:1-24.
 Djamal Boukerroui, Oliver Basset, Atilla Bskur, etc. A Multiparametric and Multiresolution Segmentation Algorithm of 3-D Ultrasonic Data (J), IEEE Transactions on Ultrasonics Ferroelectrics, and Frequency Control. 2001, Vol.48, No.1, pp:64-77.
 Sandra Jardim, Mario A. T. Figueiredo. Segmentation of fetal ultrasound images (J). Ultrasound in Medicine & Biology. 2005, Vol. 31, No.2, pp:243-250.
 Zhang Guang feng, Li Xing guo, Guo Wei. A., MMW Radiometric Image Partition Method Based on Low Brightness Temperature Target (J). Journal of Missile and Guidance. 2006, Vol.26, No.3, pp:239-241.
 Chunyan, Liu. Research on image segmentation evaluation method (D). Xidian University. 2011.