基于群体智能的思维进化算法及其在图像分割中的应用
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摘要
图像分割是图像处理和计算机视觉中的基本技术,是大多数图像分析及视觉处理的重要组成部分,也是成功进行图像分析、理解与描述的关键步骤。随着医学图像处理技术的不断发展,图像分割在医学应用中的意义越来越重要,分割后的医学图像已被广泛应用于各种领域。基于医学应用领域的特殊性及对辅助医生正确诊断疾病关系着人们生命健康的要求,准确而快速的医学图像分割具有重要的理论意义和实用价值。
本文以思维进化算法与群体智能算法为研究对象,提出基于群体智能的思维进化算法(swarm intelligence based mind evolutionary algorithm, SIMEA)并应用于图像分割领域,属于信息科学、计算机科学、自动化科学的交叉和前沿研究领域。具体包括以下几部分：
(1)在研究思维进化算法和群体智能的基础上,提出利用群体智能哲学思想改进思维进化算法,给出其一般流程,并在此框架下证明算法的收敛性。
(2)借鉴群体智能的特点,设计具体的改进思维进化算法的策略。在思维进化算法的初始阶段引入混沌序列,给出改进的混沌初始化策略,并提出定量的评价指标;提出子群体迁徙策略,使算法充分利用群体信息共享,提高算法收敛速度；提出异化过程中拥挤浓度控制策略,使算法保持种群多样性,避免算法早熟；数值优化实验表明该算法具有良好的性能。
(3)探索SIMEA在图像分割中的应用。针对Shannon信息熵存在的问题,提出了模糊指数熵的新定义,基于此定义给出了一维和二维的图像模糊指数熵,并应用于图像分割中,作为优化算法的适应度函数,利用SIMEA优化模糊隶属度函数参数,并根据隶属度分割图像,减少运算时间。
(4)进一步探索SIMEA在医学HRCT图像分割中的应用。在医学图像分割过程中,利用粒计算,加入专业医学领域知识,通过粒的粒化、合成和跳转运算,完成HRCT图像分割,在此过程中应用SIMEA优化粒的粒化,提高算法效率。
本文的创新性成果如下：
(1)提出一种基于群体智能的思维进化算法,给出算法的一般流程及收敛性证明。
(2)提出改进的混沌初始化策略,并设计三个评价初始化群体的量化指标。
(3)提出子群体迁徙策略和拥挤浓度策略,改进后的算法提高群体信息的利用,充分发挥信息共享,在提高算法收敛速度的同时保证算法全局收敛性。
(4)定义模糊指数熵及图像的一维和二维模糊指数熵,应用于图像分割,将分割过程转化为参数优化过程。
(5)提出基于SIMEA和粒计算的医学HRCT图像分割方法。
Image segmentations are foundamental technologies of image processing and computer vision. They are important parts of many image analysis and visual systems and they are also success keys for analysis, understanding and description of images. With the developments of the medical image processing technology, image segmentations in medical applications are more and more important and the segmented medical images have been widely applied in various fields. Based on the specificity of the medical applications and assistance to diagnose diseases correctly, which is relation to the people's lives and health, accurate fast segmentations of medical images have the important theoretical and practical values.
This paper studies mind evolutionary algorithm and swarm intelligence, proposes the swarm intelligence based mind evolutionary algorithm (SIMEA) and applies it in image segmentations. The study belongs to the cross-frontier research area of information science, computer science and automation science. This paper mainly includes the following sections.
(1) Based on the research of MEA and swarm intelligence, the paper improves MEA by philosophic thinking of swarm intelligence, gives its general process, and proves the convergence of the algorithm in this framework.
(2) This paper proposes specific improved strategies for MEA based on the characteristics of swarm intelligence. It introduces the chaos sequence to the initial stages of MEA, gives the improved strategy of chaotic initialization and presents three quantitative evaluation indexes. It proposes group migration strategy, which fully uses the information sharing and enhances the convergence speed. It designs dissimilation strategy with density control, which ensures population deversity and avoids premature. The experimental results of numerical optimization show that the improved algorithm has good performances.
(3) This paper proposes image segmentations based on SIMEA. For the problems of Shannon entropy, it gives a new definition of fuzzy exponent entropy, and shows fuzzy exponent image entropy with one or two dimension based on the new definition, which are used as fitness functions during image segmentation optimization. It optimizes parameters of fuzzy membership functions by SIMEA, fulfills image segmentations by membership degrees, reduces the computation time.
(4) This paper studies further the application of MEA in medical HRCT image segmentation. During the process of medical image segmentations, it introduces professional medical knowledges by granular computing, segments medical HRCT images by granulating, synthesis and conversion of granules. During the process, it applies SIMEA in granule optimizations to improve the algorithm efficiency.
The innovations of this paper are as follows.
(1) It proposes mind evolutionary algorithm based on swarm intelligence and gives a general process and the proof of convergence.
(2) It presents chaotic initialization strategy and designes three quantitative indexes for initial population evaluation.
(3) It proposed group migration strategy and density control strategy, which shares the information fully and achieves the best balance between the global convergence and the searching velocity.
(4) It defines fuzzy exponent entropy and image entropy with one or two dimension, which are applied in image segmentation and transform segmentation into parameters optimization.
(5) It shows a method of medical HRCT segmentation based on SIMEA and granular computing.
本文以思维进化算法与群体智能算法为研究对象,提出基于群体智能的思维进化算法(swarm intelligence based mind evolutionary algorithm, SIMEA)并应用于图像分割领域,属于信息科学、计算机科学、自动化科学的交叉和前沿研究领域。具体包括以下几部分：
(1)在研究思维进化算法和群体智能的基础上,提出利用群体智能哲学思想改进思维进化算法,给出其一般流程,并在此框架下证明算法的收敛性。
(2)借鉴群体智能的特点,设计具体的改进思维进化算法的策略。在思维进化算法的初始阶段引入混沌序列,给出改进的混沌初始化策略,并提出定量的评价指标;提出子群体迁徙策略,使算法充分利用群体信息共享,提高算法收敛速度；提出异化过程中拥挤浓度控制策略,使算法保持种群多样性,避免算法早熟；数值优化实验表明该算法具有良好的性能。
(3)探索SIMEA在图像分割中的应用。针对Shannon信息熵存在的问题,提出了模糊指数熵的新定义,基于此定义给出了一维和二维的图像模糊指数熵,并应用于图像分割中,作为优化算法的适应度函数,利用SIMEA优化模糊隶属度函数参数,并根据隶属度分割图像,减少运算时间。
(4)进一步探索SIMEA在医学HRCT图像分割中的应用。在医学图像分割过程中,利用粒计算,加入专业医学领域知识,通过粒的粒化、合成和跳转运算,完成HRCT图像分割,在此过程中应用SIMEA优化粒的粒化,提高算法效率。
本文的创新性成果如下：
(1)提出一种基于群体智能的思维进化算法,给出算法的一般流程及收敛性证明。
(2)提出改进的混沌初始化策略,并设计三个评价初始化群体的量化指标。
(3)提出子群体迁徙策略和拥挤浓度策略,改进后的算法提高群体信息的利用,充分发挥信息共享,在提高算法收敛速度的同时保证算法全局收敛性。
(4)定义模糊指数熵及图像的一维和二维模糊指数熵,应用于图像分割,将分割过程转化为参数优化过程。
(5)提出基于SIMEA和粒计算的医学HRCT图像分割方法。
Image segmentations are foundamental technologies of image processing and computer vision. They are important parts of many image analysis and visual systems and they are also success keys for analysis, understanding and description of images. With the developments of the medical image processing technology, image segmentations in medical applications are more and more important and the segmented medical images have been widely applied in various fields. Based on the specificity of the medical applications and assistance to diagnose diseases correctly, which is relation to the people's lives and health, accurate fast segmentations of medical images have the important theoretical and practical values.
This paper studies mind evolutionary algorithm and swarm intelligence, proposes the swarm intelligence based mind evolutionary algorithm (SIMEA) and applies it in image segmentations. The study belongs to the cross-frontier research area of information science, computer science and automation science. This paper mainly includes the following sections.
(1) Based on the research of MEA and swarm intelligence, the paper improves MEA by philosophic thinking of swarm intelligence, gives its general process, and proves the convergence of the algorithm in this framework.
(2) This paper proposes specific improved strategies for MEA based on the characteristics of swarm intelligence. It introduces the chaos sequence to the initial stages of MEA, gives the improved strategy of chaotic initialization and presents three quantitative evaluation indexes. It proposes group migration strategy, which fully uses the information sharing and enhances the convergence speed. It designs dissimilation strategy with density control, which ensures population deversity and avoids premature. The experimental results of numerical optimization show that the improved algorithm has good performances.
(3) This paper proposes image segmentations based on SIMEA. For the problems of Shannon entropy, it gives a new definition of fuzzy exponent entropy, and shows fuzzy exponent image entropy with one or two dimension based on the new definition, which are used as fitness functions during image segmentation optimization. It optimizes parameters of fuzzy membership functions by SIMEA, fulfills image segmentations by membership degrees, reduces the computation time.
(4) This paper studies further the application of MEA in medical HRCT image segmentation. During the process of medical image segmentations, it introduces professional medical knowledges by granular computing, segments medical HRCT images by granulating, synthesis and conversion of granules. During the process, it applies SIMEA in granule optimizations to improve the algorithm efficiency.
The innovations of this paper are as follows.
(1) It proposes mind evolutionary algorithm based on swarm intelligence and gives a general process and the proof of convergence.
(2) It presents chaotic initialization strategy and designes three quantitative indexes for initial population evaluation.
(3) It proposed group migration strategy and density control strategy, which shares the information fully and achieves the best balance between the global convergence and the searching velocity.
(4) It defines fuzzy exponent entropy and image entropy with one or two dimension, which are applied in image segmentation and transform segmentation into parameters optimization.
(5) It shows a method of medical HRCT segmentation based on SIMEA and granular computing.
引文
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