本文選題：Hu不變矩 + 磁共振��； 參考：《南京師范大學》2017年碩士論文

【摘要】：(1)目的:本文首先介紹了研究背景及意義,然后對磁共振(MR)圖像診斷的國內外發(fā)展現(xiàn)狀做了簡單的介紹。本文所提出的智能病腦檢測系統(tǒng)(SPBD)即是一種計算機智能輔助診斷病理核磁共振圖像系統(tǒng)。人工智能算法的研究將有助于提高檢測分類的效率和準確率,在病腦檢測領域具有十分重要的意義。本文采用神經網絡與MR圖像相結合的思路。由于神經網絡在分類訓練中,數(shù)據(jù)容易陷入局部最優(yōu)。所以本文采用了一種較新的,非常有效的捕食者-食餌粒子群算法(PP-PSO)來優(yōu)化神經網絡,從而避免了數(shù)據(jù)易陷入局部最優(yōu)問題,增強了SPBD系統(tǒng)的對新數(shù)據(jù)處理、分類的能力,實現(xiàn)了 SPBD系統(tǒng)對病腦檢測的高效,高準確率。(2)方法:本文采用DA-160數(shù)據(jù)樣本,采用Hu不變矩(HMI)來提取腦圖像特征,Hu不變矩具有平移、旋轉、比例不變性,在目標識別、圖像匹配、形狀分析等領域都有廣泛的應用。本文采用單隱層神經網絡(SLN)作為分類器。人工神經網絡(ANN)通過模仿人腦形象思維構建神經網絡,從而實現(xiàn)分布式的信息處理,具有良好的自適應、自組織和很強的自學能力,是數(shù)據(jù)分類圖像識別的有力工具。用HMI提取得到的一系列由七個特征矩組成的矩陣信息輸入SLN,經過SLN訓練,輸出的結果為非0即1的信息(0表示健康大腦圖像,1表示病腦圖像)。為了使實驗不易陷入局部最優(yōu)解,本文采用了一種基于粒子群算法(PSO)改進的優(yōu)化算法——捕食者-食餌粒子群優(yōu)化算法(PP-PSO)來訓練SLN的權值。我們將采用五折分層交叉驗證(FFSCV)來對數(shù)據(jù)進行訓練,從而保證了對有限數(shù)據(jù)集進行盡可能多的學習。最后使用分類準確率作為實驗優(yōu)良的評判標準。(3)結果:將實驗結果與其他六種較先進的SPBD算法進行比較,通過訓練輸出結果對比,發(fā)現(xiàn)本文的方法,基于捕食者一食餌粒子群算法和單隱層神經網絡算法(HMI + SLN + PP-PSO)分類效果最好,對160個數(shù)據(jù)集進行測試,靈敏度、特征度和準確率分別達到了: 96.00±5.16%,98.57±0.75%和98.25±0.65%。最后比較了 PSO和PP-PSO分別對應的準確率。其中,PSO作為該實驗的優(yōu)化算法準確率達到96.44%。(4)結論:比較發(fā)現(xiàn),HMI + SLN + PP-PSO分類性能最好,實驗結果準確率最高。而且,通過實驗結果的比較分析能發(fā)現(xiàn)HMI + SLN + PP-PSO方法的優(yōu)勢和不足,為SPBD更進一步的研究和優(yōu)化做了鋪墊。
[Abstract]:Objective: this paper first introduces the background and significance of the research, and then briefly introduces the development of MRI imaging diagnosis at home and abroad.The intelligent brain detection system (SPBDD) proposed in this paper is a computerized intelligent diagnostic system for patho-magnetic resonance imaging (MRI).The research of artificial intelligence algorithm will help to improve the efficiency and accuracy of detection and classification, which is of great significance in the field of brain disease detection.In this paper, the idea of combining neural network with Mr image is adopted.Because the neural network in the classification training, the data is easy to fall into the local optimum.So we use a new and very effective predator-prey particle swarm optimization algorithm (PP-PSO) to optimize the neural network, which avoids the data falling into the local optimal problem and enhances the ability of SPBD system to process and classify the new data.The method of high efficiency and high accuracy of SPBD system for detecting diseased brain is realized. In this paper, DA-160 data sample and Hu invariant moment are used to extract the feature of brain image. Hu invariant moment has translation, rotation, scale invariance, and is used in target recognition.Image matching, shape analysis and other fields have been widely used.In this paper, single hidden layer neural network (SLN) is used as classifier.Artificial neural network (Ann) is a powerful tool for data classification and image recognition, which can construct neural network by imitating human brain image thinking, thus realizing distributed information processing, with good self-adaptation, self-organization and strong self-learning ability.A series of matrix information, which is composed of seven characteristic moments, was extracted by HMI. After SLN training, the output result is that the information of non-zero or 1 represents the healthy brain image / 1 to represent the diseased brain image.In order to make the experiment difficult to fall into the local optimal solution, an improved particle swarm optimization algorithm based on particle swarm optimization (PSO), Predator-prey PSO (Predator-Prey PSO), is used to train the weight of SLN.We will use the FFSCV to train the data, so that we can learn as much as possible from the limited data set.Finally, the classification accuracy rate is used as the excellent criterion of the experiment. The results are compared with the other six advanced SPBD algorithms, and the method of this paper is found by comparing the results of the training output with those of the other six advanced SPBD algorithms.Based on predator-prey particle swarm optimization algorithm and single hidden layer neural network algorithm, HMI SLN PP-PSO-based classification is the best. The sensitivity, characteristic and accuracy of 160 data sets are 96.00 鹵5.1610 鹵0.75% and 98.25 鹵0.65%, respectively.Finally, the accuracy of PSO and PP-PSO are compared.Conclusion: the comparison shows that the classification performance of HMI SLN PP-PSO is the best, and the accuracy of experimental results is the highest.Furthermore, the advantages and disadvantages of the HMI SLN PP-PSO method can be found by comparing the experimental results, which pave the way for the further research and optimization of SPBD.
【學位授予單位】：南京師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R741.044;TP18

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