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Robust 2D Otsu's Algorithm for Uneven Illumination Image Segmentation.
Xing, Jiangwa; Yang, Pei; Qingge, Letu.
  • Xing J; Research Center of Basic Medical Sciences, Medical College, Qinghai University, Xining 810016, China.
  • Yang P; Department of Computer Technology and Application, Qinghai University, Xining 810016, China.
  • Qingge L; Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA.
Comput Intell Neurosci ; 2020: 5047976, 2020.
Article in English | MEDLINE | ID: covidwho-833098
ABSTRACT
Otsu's algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu's method is more robust compared to 1D Otsu's method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu's algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresholding are adopted to find the best segmentation result. Experiments are conducted on both synthetic and real world uneven illumination images as well as real world regular illumination cell images. Original 2D Otsu's method, MAOTSU_2D, and two latest 1D Otsu's methods (Cao's method and DVE) are included for comparisons. Both qualitative and quantitative evaluations are introduced to verify the effectiveness of the proposed method. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. For a test group of seven real world uneven illumination images, the proposed method could lower the ME value by 15% and increase the DSC value by 10%.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Algorithms / Image Processing, Computer-Assisted / Lighting Type of study: Experimental Studies / Qualitative research Language: English Journal: Comput Intell Neurosci Journal subject: Medical Informatics / Neurology Year: 2020 Document Type: Article Affiliation country: 2020

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Algorithms / Image Processing, Computer-Assisted / Lighting Type of study: Experimental Studies / Qualitative research Language: English Journal: Comput Intell Neurosci Journal subject: Medical Informatics / Neurology Year: 2020 Document Type: Article Affiliation country: 2020