Automatic Kidney CT Images Segmentation Algorithm Based on 3D Fuzzy Connectedness and Pulse Coupled Neural Network

BAI Peirui; LI Zheng; LIU Qingyi; WANG Meng; BI Lijun; REN Yande; WANG Chengjian

doi:10.11999/JEIT221252

Volume 45 Issue 6

Jun. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(6): 2264-2272

BAI Peirui, LI Zheng, LIU Qingyi, WANG Meng, BI Lijun, REN Yande, WANG Chengjian. Automatic Kidney CT Images Segmentation Algorithm Based on 3D Fuzzy Connectedness and Pulse Coupled Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2264-2272. doi: 10.11999/JEIT221252

Citation:

BAI Peirui, LI Zheng, LIU Qingyi, WANG Meng, BI Lijun, REN Yande, WANG Chengjian. Automatic Kidney CT Images Segmentation Algorithm Based on 3D Fuzzy Connectedness and Pulse Coupled Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2264-2272. doi: 10.11999/JEIT221252

Citation:

BAI Peirui, LI Zheng, LIU Qingyi, WANG Meng, BI Lijun, REN Yande, WANG Chengjian. Automatic Kidney CT Images Segmentation Algorithm Based on 3D Fuzzy Connectedness and Pulse Coupled Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2264-2272. doi: 10.11999/JEIT221252

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Automatic Kidney CT Images Segmentation Algorithm Based on 3D Fuzzy Connectedness and Pulse Coupled Neural Network

doi: 10.11999/JEIT221252

1.
School of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2.
Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao 265000, China

Funds: The National Natural Science Foundation of China (61471225)

Received Date: 2022-09-27
Rev Recd Date: 2022-12-08

Available Online: 2022-12-09

Publish Date: 2023-06-10

Abstract

Abstract

Automatic and accurate segmentation of 3D kidney CT image is of great significance to reduce the workload of doctors and improve the efficiency of computer-aided diagnosis. However, due to the structural complexity of kidney organs and the gray similarity of adjacent parts, accurate segmentation of 3D kidney is still challenging. Based on the characteristics of simple structure and few parameters of Simplified Pulse Coupled Neural Network (SPCNN), combined with Fuzzy Connectedness (FC) algorithm, an automatic segmentation algorithm of three-dimensional kidney CT images is proposed in this paper. The main contributions of this paper are as follows: The 2D SPCNN is extended to 3D SPCNN, which can make full use of the inter-layer information of 3D CT images. A 3D seed point automatic generation strategy based on the centroid of region of interest is proposed, which can effectively improve the automatic segmentation efficiency of the algorithm. Effective coupling of 3D FC response map and 3D SPCNN is realized. The proposed algorithm is validated on self-made and public datasets, and the results show that the performance of the proposed algorithm is better than that of the existing mainstream algorithms. The average values of Dice coefficient, accuracy, sensitivity, volume error and average symmetric surface distance can achieve 0.9095, 0.9969, 0.8517, 0.1749 and 0.8536 respectively.
- 3D CT images,
- Kidney segmentation,
- Pulse Coupled Neural Network (PCNN),
- Fuzzy Connectedness (FC)

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References(21)

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