本文選題：醫(yī)學(xué)圖像分割 + 模糊C-均值聚類��； 參考：《東華大學(xué)》2017年碩士論文

【摘要】：醫(yī)學(xué)圖像分割一直是近幾年研究的熱點(diǎn)問題,由于受到成像設(shè)備等外界因素的干擾,醫(yī)學(xué)圖像呈現(xiàn)邊界模糊,強(qiáng)度不均勻的特點(diǎn),影響了醫(yī)生對(duì)病情的診斷。因此如何進(jìn)行快速、有效、準(zhǔn)確的圖像分割對(duì)后續(xù)臨床分析起著至關(guān)重要的作用。由于圖像自身的模糊不確定性,一些學(xué)者將模糊理論引入圖像處理中,利用模糊聚類進(jìn)行圖像分割。其中,模糊C-均值聚類算法(fuzzy C-means algorithm, FCM)應(yīng)用最為廣泛,但是該算法在運(yùn)行過程中需要提前確定初始聚類中心和聚類數(shù),并且對(duì)噪聲比較敏感,容易陷入局部最優(yōu)解。人工免疫算法(AIS)繼承生物免疫系統(tǒng)優(yōu)良特性,具有分布式并行處理,快速收斂性,全局尋優(yōu)等特點(diǎn),在諸多領(lǐng)域得到成功的應(yīng)用。本文結(jié)合人工免疫系統(tǒng)和聚類問題進(jìn)行研究,提出一種新的免疫聚類算法,主要改進(jìn)如下：1、噪聲會(huì)嚴(yán)重影響算法的執(zhí)行效率和分割效果,在進(jìn)行醫(yī)學(xué)圖像分割之前加入改進(jìn)型的開關(guān)極值中值濾波,改進(jìn)的濾波算法能夠有效識(shí)別噪聲點(diǎn)和有用數(shù)據(jù)點(diǎn)。通過實(shí)驗(yàn)證明,改進(jìn)型的算法實(shí)現(xiàn)了既能去除噪聲,同時(shí)可以很好的保護(hù)圖像邊緣細(xì)節(jié)。2、為能夠提前確定較為準(zhǔn)確的初始聚類中心和聚類數(shù),對(duì)灰度直方圖通過插值法進(jìn)行平滑,能夠有效過濾偽峰點(diǎn),進(jìn)而通過峰值檢測(cè)得到優(yōu)秀的初始聚類中心和圖像的聚類數(shù),通過多中心組合的方式提高算法準(zhǔn)確性,避免陷入局部極值。通過實(shí)驗(yàn)證明,改進(jìn)后的FCM算法更加穩(wěn)定,分割精度更高。3、對(duì)基本克隆選擇算法引入抗體種群濃度調(diào)節(jié)機(jī)制,既保證種群不斷的向優(yōu)良的特性發(fā)展,又能避免種群過度單一化,有效的保持抗體多樣性。再結(jié)合高斯變異和柯西變異的特點(diǎn),提出一種混合自適應(yīng)變異——高斯-柯西混合自適應(yīng)變異,能夠動(dòng)態(tài)調(diào)節(jié)變異步長(zhǎng),避免算法陷入局部最優(yōu)解,進(jìn)一步提高算法的全局尋優(yōu)能力。最后充分利用免疫記憶機(jī)制,不斷保存優(yōu)秀抗體,替換差的抗體,使得算法不斷向良性發(fā)展。通過實(shí)驗(yàn)測(cè)試,改進(jìn)后的算法全局尋優(yōu)能力和收斂速度得到有效改善。4、將改進(jìn)的克隆選擇算法優(yōu)化改進(jìn)后的FCM算法,再配合改進(jìn)的濾波算法,與傳統(tǒng)FCM算法比較。新算法的抗噪能力、收斂速度、全局尋優(yōu)能力、分割精度都得到顯著提升。
[Abstract]:Medical image segmentation has been a hot issue in recent years. Due to the interference of imaging equipment and other external factors, medical image presents the characteristics of blurred boundary and uneven intensity, which affects the doctor's diagnosis of the disease. Therefore, how to carry out fast, effective and accurate image segmentation plays an important role in subsequent clinical analysis. Due to the fuzzy uncertainty of image itself, some scholars introduce fuzzy theory into image processing and use fuzzy clustering to segment images. Among them, fuzzy C-means clustering algorithm (fuzzy C-means algorithm) is the most widely used, but it needs to determine the initial clustering center and the number of clusters in advance in the running process, and is sensitive to noise, so it is easy to fall into the local optimal solution. Artificial immune algorithm (AIS) inherits the excellent characteristics of biological immune system, and has the characteristics of distributed parallel processing, fast convergence, global optimization and so on, and has been successfully applied in many fields. In this paper, the artificial immune system and clustering problems are studied, and a new immune clustering algorithm is proposed. The main improvements are as follows: 1. Noise will seriously affect the efficiency and segmentation effect of the algorithm. An improved switching extremum median filter is added before medical image segmentation. The improved filtering algorithm can effectively identify noise points and useful data points. The experimental results show that the improved algorithm can not only remove noise, but also protect image edge detail. 2. In order to determine the accurate initial clustering center and clustering number in advance, the improved algorithm can not only remove the noise, but also protect the image edge details. The gray histogram is smoothed by interpolation method, which can filter pseudo peak points effectively, and then obtain excellent initial clustering center and image clustering number by peak detection, and improve the accuracy of the algorithm by multi-center combination. Avoid falling into local extremum. It is proved by experiments that the improved FCM algorithm is more stable and has higher segmentation accuracy. The introduction of antibody population concentration regulation mechanism to the basic clone selection algorithm can not only ensure the population to develop to excellent characteristics, but also avoid the excessive singularity of the population. Effectively maintain antibody diversity. Combined with the characteristics of Gao Si mutation and Cauchy mutation, a hybrid adaptive mutation-Gauss-Cauchy hybrid adaptive mutation is proposed, which can dynamically adjust the step size of the mutation and avoid the algorithm falling into a local optimal solution. The global optimization ability of the algorithm is further improved. Finally, we make full use of the immune memory mechanism, keep the excellent antibody and replace the bad antibody, so that the algorithm continues to develop benign. The global optimization ability and convergence speed of the improved algorithm are improved by experiments. The improved clone selection algorithm is optimized by the improved FCM algorithm, and the improved filter algorithm is combined with the traditional FCM algorithm to compare with the traditional FCM algorithm. The performance of the new algorithm, such as anti-noise, convergence speed, global optimization and segmentation accuracy, has been improved significantly.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP18;R44

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本文編號(hào)：2057100