A Novel Histogram Region Merging Based Multithreshold Segmentation Algorithm for MR Brain Images.

IF 3.3 Q2 ENGINEERING, BIOMEDICAL

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-03-16 DOI:10.1155/2017/9759414

Siyan Liu, Xuanjing Shen, Yuncong Feng, Haipeng Chen

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引用次数: 10

Abstract

Multithreshold segmentation algorithm is time-consuming, and the time complexity will increase exponentially with the increase of thresholds. In order to reduce the time complexity, a novel multithreshold segmentation algorithm is proposed in this paper. First, all gray levels are used as thresholds, so the histogram of the original image is divided into 256 small regions, and each region corresponds to one gray level. Then, two adjacent regions are merged in each iteration by a new designed scheme, and a threshold is removed each time. To improve the accuracy of the merger operation, variance and probability are used as energy. No matter how many the thresholds are, the time complexity of the algorithm is stable at O(L). Finally, the experiment is conducted on many MR brain images to verify the performance of the proposed algorithm. Experiment results show that our method can reduce the running time effectively and obtain segmentation results with high accuracy.

Abstract Image

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一种新的基于直方图区域合并的脑磁共振图像多阈值分割算法。

多阈值分割算法耗时长，时间复杂度随着阈值的增大呈指数增长。为了降低时间复杂度，本文提出了一种新的多阈值分割算法。首先，将所有灰度级别作为阈值，将原始图像的直方图划分为256个小区域，每个区域对应一个灰度级别。然后，采用新设计的方案，在每次迭代中合并两个相邻区域，并去除一个阈值。为了提高合并操作的准确性，采用方差和概率作为能量。无论阈值有多少，算法的时间复杂度稳定在0 (L)。最后，在多幅脑磁共振图像上进行了实验，验证了算法的性能。实验结果表明，该方法可以有效地减少运行时间，获得精度较高的分割结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biomedical Imaging ENGINEERING, BIOMEDICAL-

CiteScore

12.00

自引率

0.00%

发文量

审稿时长

20 weeks

期刊介绍： The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics