Author:
Feng Yuncong,Liu Yunfei,Liu Zhicheng,Liu Wanru,Yao Qingan,Zhang Xiaoli
Abstract
Medical image segmentation is widely used in clinical medicine, and the accuracy of the segmentation algorithm will affect the diagnosis results and treatment plans. However, manual segmentation of medical images requires extensive experience and knowledge, and it is both time-consuming and labor-intensive. To overcome the problems above, we propose a novel interval iterative multi-thresholding segmentation algorithm based on hybrid spatial filter and region growing for medical brain MR images. First, a hybrid spatial filter is designed to perform on the original image, which can make full use of the spatial information while denoising. Second, the interval iterative Otsu method based on region growing is proposed to segment the original image and its filtering layer. The initial thresholds can be quickly obtained by region growing algorithm, which can reduce the time complexity. The interval iterative algorithm is used to optimize the thresholds. Finally, a weighted strategy is used to refine the segmentation results. The segmentation results of our proposed algorithm outperform other comparison algorithms in both subjective and objective evaluations. Subjectively, the obtained segmentation results have clear edges, complete and consistent regions. We use the uniformity measure (U) for objective evaluation, and the U value is significantly higher than other comparison algorithms. The proposed algorithm achieved an average U value of 0.9854 across all test images. The proposed algorithm can segment medical images well and expand the doctor’s ability to utilize medical images.
Funder
Natural Science Foundation of Jilin Province
Jilin Provincial Department of Science and Technology
Education Department of Jilin Province
Fundamental Research Funds for the Central Universities JLU
National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference43 articles.
1. Mărginean, R., Andreica, A., Dioşan, L., and Bálint, Z. (2020). Butterfly effect in chaotic image segmentation. Entropy, 22.
2. Anatomically constrained neural networks (ACNNs): Application to cardiac image enhancement and segmentation;Oktay;IEEE Trans. Med. Imaging,2017
3. A survey on regional level set image segmentation models based on the energy functional similarity measure;Zou;Neurocomputing,2021
4. Improving the detection of autism spectrum disorder by combining structural and functional MRI information;Cabezas;NeuroImage Clin.,2020
5. Survey on medical image computer aided detection and diagnosis systems;Zheng;Ruan Jian Xue Bao J. Softw.,2018
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