基于混合模型的醫(yī)學(xué)圖像聚類的分類數(shù)的估計(jì).doc
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基于混合模型的醫(yī)學(xué)圖像聚類的分類數(shù)的估計(jì),1.56萬字自己原創(chuàng)的畢業(yè)論文，已經(jīng)通過校內(nèi)系統(tǒng)檢測，重復(fù)率低，僅在本站獨(dú)家出售，大家放心下載使用摘要 聚類分析研究已有篅@さ睦罰┠昀?，其重要屑s捌溲芯糠較虻慕徊嫘緣玫攪巳嗣塹目隙ā＞劾嗍鞘萃誥虻鬧匾芯磕諶葜?，灾o侗鶚蕕哪讜誚峁狗矯嬗兇偶渲匾淖饔謾�...
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基于混合模型的醫(yī)學(xué)圖像聚類的分類數(shù)的估計(jì)
1.56萬字
自己原創(chuàng)的畢業(yè)論文，已經(jīng)通過校內(nèi)系統(tǒng)檢測，重復(fù)率低，僅在本站獨(dú)家出售，大家放心下載使用
摘要 聚類分析研究已有篅@さ睦罰┠昀矗渲匾約捌溲芯糠較虻慕徊嫘緣玫攪巳嗣塹目隙?。聚类是数据驼a虻鬧匾芯磕諶葜?，灾o侗鶚蕕哪讜誚峁狗矯嬗兇偶渲匾淖饔謾�
醫(yī)學(xué)圖像聚類技術(shù)是醫(yī)學(xué)圖像處理與可視化的一項(xiàng)關(guān)鍵技術(shù)，而基于混合模型對醫(yī)學(xué)圖像進(jìn)行正確聚類分析，則可以輔助醫(yī)生更深入了解病患組織，從而為更好的進(jìn)行臨床診斷與手術(shù)規(guī)劃提供了良好的理論支持。醫(yī)學(xué)圖像分析是醫(yī)療診斷、藥物反應(yīng)監(jiān)控和疾病管理等最重要的輔助手段，具有速度快、非入侵、幾乎沒有副作用、花費(fèi)低、效果好等優(yōu)勢。由于人體解剖結(jié)構(gòu)的復(fù)雜性、軟組織的不規(guī)則性，以及成像質(zhì)量受到多種因素的影響，使得醫(yī)學(xué)圖像分析和理解成為一個難點(diǎn)，醫(yī)學(xué)圖像聚類分析是醫(yī)學(xué)圖像分析和理解中的重要技術(shù)。
目前，醫(yī)學(xué)圖像聚類算法還沒有達(dá)到理想的識別效果，不能完全滿足醫(yī)學(xué)圖像分析和理解的要求。本文試圖研究醫(yī)學(xué)圖像分類數(shù)的估計(jì)及基于高斯混合模型的聚類方法。首先是基于In-Group Proportion(IGP）指標(biāo)估計(jì)出醫(yī)學(xué)圖像的分類數(shù)，然后通過高斯混合模型和期望最大值（Expectation Maximization，EM）算法實(shí)現(xiàn)醫(yī)學(xué)圖像的聚類，通過得到的不同聚類圖來驗(yàn)證IGP指標(biāo)的準(zhǔn)確性與有效性。
本方法適合用于醫(yī)學(xué)圖像識別以及混合模型聚類方法及其算法，通過對醫(yī)學(xué)圖像的指標(biāo)分析，找到最合適的醫(yī)學(xué)圖像分類數(shù)，使得經(jīng)過聚類分類處理過的圖像對實(shí)際的醫(yī)學(xué)分析與病理診斷產(chǎn)生最大最廣泛的實(shí)際效用。
關(guān)鍵詞：醫(yī)學(xué)圖像 高斯混合模型 聚類分析 分類數(shù)
Estimation the Number of Medical Image Classification Based on the Mixture Model Clustering
Abstract Clustering analysis has a long history, In recent years, its importance and interdisciplinary research direction to get people's recognition. Clustering is one of the important research data mining, has an extremely important role in identifying the internal structure of the data.
Medical image clustering technology is medical image processing and visualization of a key technology, and the hybrid model based on the medical image clustering correctly estimate the number of categories, you can help doctors better understanding of patient organizations, so as to better clinical diagnosis and surgical planning provides a good theoretical support. Medical image analysis is a medical diagnosis, drug reactions most important adjunct to monitoring and disease management, etc., with fast, non-invasive, virtually no side effects, low cost, good effect and other advantages. Because of the complexity, soft tissue irregularities, human anatomy and image quality is affected by many factors, making medical image analysis and understanding become a difficult, cluster analysis of medical image analysis and understanding of medical images is an important technology.
Medical image clustering algorithm has not yet reached the desired recognition results, can not fully meet the medical image analysis and understanding of the requirements. This paper attempts to study estimated the number of medical image classification and clustering methods based on Gaussian mixture model-based (In-Group Proportion, IGP) indicators to estimate the number of categories of medical images, and the maximum value (Expectation Maximization Gaussian mixture model by expectation, number of image classification EM) algorithm into the medical image has been obtained in the corresponding cluster diagram and through experiments verify the accuracy and effectiveness of IGP indicators.
This method is suitable for medical image recognition and clustering method hybrid model and its algorithm, by indicators of medical image analysis, to find the most suitable number of categories of medical images such processed through clustering classification of medical image analysis and the actual Pathological diagnosis produces the largest and most extensive practical utility.
Key words: Medical image, Gaussian mixture model, Cluster analysis, Classification Number
目 錄
第一章 緒論 1
1.1 課題研究背景 1
1.2 國內(nèi)外發(fā)展現(xiàn)狀 2
1.3 論文的內(nèi)容安排 3
第二章 聚類分析 4
2.1聚類的基本概念 4
2.1.1 聚類的定義 4
2.1.2 聚類分析的特征 5
2.1.3 聚類分析的應(yīng)用 6
2.1.4 聚類算法的要求 7
2.2 聚類的方法 8
2.2.1 劃分方法 9
2.2.2 層次方法 9
2.2.3 基于密度的方法 9
2.2.4 基于網(wǎng)格的方法 10
2.2.5 基于模型的方法 10
2.3聚類的準(zhǔn)則函數(shù) 11
2.3.1 類內(nèi)距離準(zhǔn)則 11
2.3.2 類間距離準(zhǔn)則 11
2.4 本章小結(jié) 12
第三章 醫(yī)學(xué)圖像分類數(shù)的估計(jì)和聚類算法 13
3.1 高斯混合模型和EM算法 13
3.1.1.高斯混合模型 13
3.1.2 EM算法 13
3.2 模型的選擇 15
3.3 基于IGP指標(biāo)的醫(yī)學(xué)圖像聚類研究 16
3.3.1 IGP指標(biāo)相關(guān)知識 16
3.3.2 基于IGP指標(biāo)的分類數(shù)的估計(jì) 17
3.3.3 基于IGP指標(biāo)的醫(yī)學(xué)圖像聚類 20
3.4 本章小結(jié) 24
第四章 結(jié)論 25
4.1 工作總結(jié) 25
4.2 后期工作的展望 25
致 謝 26
參考文獻(xiàn) 27
1.56萬字
自己原創(chuàng)的畢業(yè)論文，已經(jīng)通過校內(nèi)系統(tǒng)檢測，重復(fù)率低，僅在本站獨(dú)家出售，大家放心下載使用
摘要 聚類分析研究已有篅@さ睦罰┠昀矗渲匾約捌溲芯糠較虻慕徊嫘緣玫攪巳嗣塹目隙?。聚类是数据驼a虻鬧匾芯磕諶葜?，灾o侗鶚蕕哪讜誚峁狗矯嬗兇偶渲匾淖饔謾�
醫(yī)學(xué)圖像聚類技術(shù)是醫(yī)學(xué)圖像處理與可視化的一項(xiàng)關(guān)鍵技術(shù)，而基于混合模型對醫(yī)學(xué)圖像進(jìn)行正確聚類分析，則可以輔助醫(yī)生更深入了解病患組織，從而為更好的進(jìn)行臨床診斷與手術(shù)規(guī)劃提供了良好的理論支持。醫(yī)學(xué)圖像分析是醫(yī)療診斷、藥物反應(yīng)監(jiān)控和疾病管理等最重要的輔助手段，具有速度快、非入侵、幾乎沒有副作用、花費(fèi)低、效果好等優(yōu)勢。由于人體解剖結(jié)構(gòu)的復(fù)雜性、軟組織的不規(guī)則性，以及成像質(zhì)量受到多種因素的影響，使得醫(yī)學(xué)圖像分析和理解成為一個難點(diǎn)，醫(yī)學(xué)圖像聚類分析是醫(yī)學(xué)圖像分析和理解中的重要技術(shù)。
目前，醫(yī)學(xué)圖像聚類算法還沒有達(dá)到理想的識別效果，不能完全滿足醫(yī)學(xué)圖像分析和理解的要求。本文試圖研究醫(yī)學(xué)圖像分類數(shù)的估計(jì)及基于高斯混合模型的聚類方法。首先是基于In-Group Proportion(IGP）指標(biāo)估計(jì)出醫(yī)學(xué)圖像的分類數(shù)，然后通過高斯混合模型和期望最大值（Expectation Maximization，EM）算法實(shí)現(xiàn)醫(yī)學(xué)圖像的聚類，通過得到的不同聚類圖來驗(yàn)證IGP指標(biāo)的準(zhǔn)確性與有效性。
本方法適合用于醫(yī)學(xué)圖像識別以及混合模型聚類方法及其算法，通過對醫(yī)學(xué)圖像的指標(biāo)分析，找到最合適的醫(yī)學(xué)圖像分類數(shù)，使得經(jīng)過聚類分類處理過的圖像對實(shí)際的醫(yī)學(xué)分析與病理診斷產(chǎn)生最大最廣泛的實(shí)際效用。
關(guān)鍵詞：醫(yī)學(xué)圖像 高斯混合模型 聚類分析 分類數(shù)
Estimation the Number of Medical Image Classification Based on the Mixture Model Clustering
Abstract Clustering analysis has a long history, In recent years, its importance and interdisciplinary research direction to get people's recognition. Clustering is one of the important research data mining, has an extremely important role in identifying the internal structure of the data.
Medical image clustering technology is medical image processing and visualization of a key technology, and the hybrid model based on the medical image clustering correctly estimate the number of categories, you can help doctors better understanding of patient organizations, so as to better clinical diagnosis and surgical planning provides a good theoretical support. Medical image analysis is a medical diagnosis, drug reactions most important adjunct to monitoring and disease management, etc., with fast, non-invasive, virtually no side effects, low cost, good effect and other advantages. Because of the complexity, soft tissue irregularities, human anatomy and image quality is affected by many factors, making medical image analysis and understanding become a difficult, cluster analysis of medical image analysis and understanding of medical images is an important technology.
Medical image clustering algorithm has not yet reached the desired recognition results, can not fully meet the medical image analysis and understanding of the requirements. This paper attempts to study estimated the number of medical image classification and clustering methods based on Gaussian mixture model-based (In-Group Proportion, IGP) indicators to estimate the number of categories of medical images, and the maximum value (Expectation Maximization Gaussian mixture model by expectation, number of image classification EM) algorithm into the medical image has been obtained in the corresponding cluster diagram and through experiments verify the accuracy and effectiveness of IGP indicators.
This method is suitable for medical image recognition and clustering method hybrid model and its algorithm, by indicators of medical image analysis, to find the most suitable number of categories of medical images such processed through clustering classification of medical image analysis and the actual Pathological diagnosis produces the largest and most extensive practical utility.
Key words: Medical image, Gaussian mixture model, Cluster analysis, Classification Number
目 錄
第一章 緒論 1
1.1 課題研究背景 1
1.2 國內(nèi)外發(fā)展現(xiàn)狀 2
1.3 論文的內(nèi)容安排 3
第二章 聚類分析 4
2.1聚類的基本概念 4
2.1.1 聚類的定義 4
2.1.2 聚類分析的特征 5
2.1.3 聚類分析的應(yīng)用 6
2.1.4 聚類算法的要求 7
2.2 聚類的方法 8
2.2.1 劃分方法 9
2.2.2 層次方法 9
2.2.3 基于密度的方法 9
2.2.4 基于網(wǎng)格的方法 10
2.2.5 基于模型的方法 10
2.3聚類的準(zhǔn)則函數(shù) 11
2.3.1 類內(nèi)距離準(zhǔn)則 11
2.3.2 類間距離準(zhǔn)則 11
2.4 本章小結(jié) 12
第三章 醫(yī)學(xué)圖像分類數(shù)的估計(jì)和聚類算法 13
3.1 高斯混合模型和EM算法 13
3.1.1.高斯混合模型 13
3.1.2 EM算法 13
3.2 模型的選擇 15
3.3 基于IGP指標(biāo)的醫(yī)學(xué)圖像聚類研究 16
3.3.1 IGP指標(biāo)相關(guān)知識 16
3.3.2 基于IGP指標(biāo)的分類數(shù)的估計(jì) 17
3.3.3 基于IGP指標(biāo)的醫(yī)學(xué)圖像聚類 20
3.4 本章小結(jié) 24
第四章 結(jié)論 25
4.1 工作總結(jié) 25
4.2 后期工作的展望 25
致 謝 26
參考文獻(xiàn) 27
