發(fā)布時(shí)間：2018-04-13 06:09

  本文選題：模糊聚類 + 醫(yī)學(xué)圖像分割��； 參考：《西南科技大學(xué)》2017年碩士論文

【摘要】：圖像分割是圖像處理領(lǐng)域的重要研究內(nèi)容,普遍應(yīng)用于醫(yī)學(xué)、氣象、計(jì)算機(jī)視覺、軍事、遙感等諸多研究領(lǐng)域。醫(yī)學(xué)圖像分割就是對醫(yī)學(xué)圖像進(jìn)行有意義的劃分,使其成為指定個數(shù)的相異區(qū)域,每個區(qū)域內(nèi)都保持一致性,區(qū)域之間盡可能地不同,并且各區(qū)域互不相交。醫(yī)學(xué)圖像分割為特征提取與識別、三維可視化、病理分析與診斷等提供有用的信息。醫(yī)學(xué)圖像分割的主要目的是對醫(yī)學(xué)圖像進(jìn)行有醫(yī)療價(jià)值的劃分,并提取出特定的區(qū)域,便于醫(yī)生制定醫(yī)療方案,開展療效評估等。醫(yī)學(xué)圖像在成像時(shí)會受到分辨率、光照條件等影響,因此存在不確定性,而模糊處理技術(shù)正好適用于此類問題。本文深入研究了模糊聚類基本理論以及其它人工智能技術(shù),分析了前人算法中存在的問題以及在醫(yī)學(xué)圖像分割中遇到的困難,提出了幾種改進(jìn)的模糊聚類算法,并且應(yīng)用于醫(yī)學(xué)圖像分割中。本文主要獲得的創(chuàng)新性成果如下:(1)在現(xiàn)有的相關(guān)評價(jià)標(biāo)準(zhǔn)的基礎(chǔ)上,本文提出了新的聚類算法評價(jià)標(biāo)準(zhǔn):聚類中心變化值指數(shù)dv。作為衡量一個聚類算法優(yōu)劣的新標(biāo)準(zhǔn),dv指數(shù)適用于所有基于聚類中心初始化的聚類算法,不僅僅是模糊聚類,也不僅僅是圖像分割;關(guān)注的是以何種方法尋找的初始聚類中心更好,從結(jié)果的角度或者算法整體的角度評判一個聚類算法的優(yōu)劣。(2)在快速模糊聚類算法FFCM和En FCM基礎(chǔ)上,本文提出了一種更加高效的醫(yī)學(xué)圖像分割算法。該算法先將圖像的直方圖與高斯模板卷積,并進(jìn)行峰值檢測,得到c個峰值的橫坐標(biāo),以此為基礎(chǔ)進(jìn)行區(qū)間劃分,在各區(qū)間范圍內(nèi)初始化并且更新聚類中心,然后對原圖像進(jìn)行均值濾波,最后根據(jù)像素值所屬區(qū)間完成分割。實(shí)驗(yàn)證明,在保證分割質(zhì)量的前提下,可將運(yùn)行時(shí)間減少4%以上,優(yōu)于目前的快速分割算法。(3)充分挖掘利用圖像直方圖的區(qū)間信息,得出了兩種新的基于模糊聚類的醫(yī)學(xué)圖像分割算法。首先用區(qū)域分裂求初始聚類中心或者區(qū)域迭代均值化求初始聚類中心,簡化隸屬度的初始化方式,采用En FCM的方法更新隸屬度,并提出了對隸屬度進(jìn)行選擇性計(jì)算的策略,進(jìn)一步減少了算法的計(jì)算量,提高了運(yùn)行效率;再由隸屬度的變化更新相應(yīng)區(qū)間的范圍,限制聚類中心的計(jì)算于每個區(qū)間中,直到前后兩次聚類中心的變化值小于指定的閾值,迭代結(jié)束,完成分割。通過實(shí)驗(yàn)證明,兩種新算法均不同程度地優(yōu)于前述相關(guān)算法,尤其是第二種新算法在其他參數(shù)表現(xiàn)穩(wěn)定的情況下,效率也是最優(yōu)的。(4)利用隸屬度的選擇性計(jì)算來改進(jìn)qjjc FCM算法和gsav FCM算法。前者改進(jìn)后優(yōu)于改進(jìn)前,而后者改進(jìn)后的表現(xiàn)不佳。我們分析了原因,或與其尋找初始聚類中心的方式和區(qū)間劃分方法有關(guān)。
[Abstract]:Image segmentation is an important research content in the field of image processing. It is widely used in many fields such as medicine, meteorology, computer vision, military affairs, remote sensing and so on.Medical image segmentation is a meaningful division of medical image, making it a designated number of different regions, each region is consistent, the regions are as different as possible, and the regions do not intersect each other.Medical image segmentation provides useful information for feature extraction and recognition, 3D visualization, pathological analysis and diagnosis.The main purpose of medical image segmentation is to divide the medical image with medical value, and extract the specific region, which is convenient for doctors to formulate medical plan and evaluate the curative effect.Medical images are affected by resolution, illumination conditions and so on, so there is uncertainty, and fuzzy processing technology is suitable for this kind of problems.In this paper, the basic theory of fuzzy clustering and other artificial intelligence techniques are deeply studied, the problems existing in previous algorithms and the difficulties encountered in medical image segmentation are analyzed, and several improved fuzzy clustering algorithms are proposed.And it is applied to medical image segmentation.The main innovative results obtained in this paper are as follows: (1) on the basis of the existing evaluation criteria, a new evaluation criterion of clustering algorithm is proposed: the index of variation value of clustering center (dv).As a new standard for evaluating the advantages and disadvantages of a clustering algorithm, the DV index is suitable for all clustering algorithms based on clustering center initialization, not only fuzzy clustering, but also not only image segmentation;The focus is on which method is better to find the initial clustering center. Judging the advantages and disadvantages of a clustering algorithm from the point of view of the result or the whole algorithm) based on the fast fuzzy clustering algorithms FFCM and en FCM.In this paper, a more efficient medical image segmentation algorithm is proposed.The algorithm firstly convolution the histogram of the image with Gao Si template, and detect the peak value, get the transverse coordinate of the c peak value, and then divide the interval, initialize and update the cluster center in the range of each interval.Then the original image is filtered by the mean value, and the segmentation is completed according to the interval of the pixel value.Experiments show that the running time can be reduced by more than 4% under the premise of ensuring segmentation quality, which is better than the current fast segmentation algorithm.Two new medical image segmentation algorithms based on fuzzy clustering are proposed.At first, the initial cluster center is obtained by region splitting or region iteration mean value, the initialization of membership degree is simplified, the membership degree is updated by en FCM method, and the strategy of selective calculation of membership degree is put forward.It further reduces the computational cost of the algorithm and improves the running efficiency. Then the range of the corresponding interval is updated by the change of the membership degree, and the calculation of the cluster center is limited to each interval, until the change value of the two clustering centers is less than the specified threshold.At the end of the iteration, the partition is completed.The experiments show that the two new algorithms are better than the previous algorithms to some extent, especially the second new algorithm is stable in the case of other parameters.Qjjc FCM algorithm and gsav FCM algorithm are improved by the selective calculation of membership degree.The former is better than the former, while the latter is not good after the improvement.The reason is analyzed, or it is related to the method of searching for the initial cluster center and the method of interval partition.
【學(xué)位授予單位】：西南科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 周鵬飛;張?jiān)虑?;基于模糊BP神經(jīng)網(wǎng)絡(luò)的圖像分割方法研究[J];計(jì)算機(jī)工程與設(shè)計(jì);2014年07期

2 段娟;景萬里;張楠;肖創(chuàng)柏;武博;;基于小波變換的多方向加權(quán)聚類顱內(nèi)腫瘤圖像分割方法[J];計(jì)算機(jī)應(yīng)用與軟件;2014年04期

3 唐春菊;劉衍平;;基于遺傳算法的類間方差圖像閾值分割[J];攀枝花學(xué)院學(xué)報(bào);2012年05期

4 周長英;;基于改進(jìn)的模糊BP神經(jīng)網(wǎng)絡(luò)圖像分割算法[J];計(jì)算機(jī)仿真;2011年04期

5 王桂娟;楊加峰;王保保;;基于無需重新初始化CV模型的醫(yī)學(xué)圖像分割[J];電子科技;2010年02期

6 張煜東;吳樂南;韋耿;;基于粒子群神經(jīng)網(wǎng)絡(luò)的細(xì)胞圖像分割方法[J];電子測量與儀器學(xué)報(bào);2009年07期

7 陳華;管樂樂;陳秉巖;劉志軍;;基于人工神經(jīng)網(wǎng)絡(luò)的聚類算法[J];安徽理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2009年02期

8 李志梅;肖德貴;;快速模糊C均值聚類的圖像分割方法[J];計(jì)算機(jī)工程與應(yīng)用;2009年12期

9 盧珊萍;于盛林;;基于粒子群算法的細(xì)胞神經(jīng)網(wǎng)絡(luò)模板參數(shù)設(shè)計(jì)[J];計(jì)算機(jī)技術(shù)與發(fā)展;2009年04期

10 溫淑煥;唐英干;;基于Fisher準(zhǔn)則的多閾值圖像分割方法[J];激光與紅外;2008年07期

相關(guān)博士學(xué)位論文 前2條

1 張小峰;基于模糊聚類算法的醫(yī)學(xué)圖像分割技術(shù)研究[D];山東大學(xué);2014年

2 劉軍偉;基于水平集的圖像分割方法研究及其在醫(yī)學(xué)圖像中的應(yīng)用[D];中國科學(xué)技術(shù)大學(xué);2009年

相關(guān)碩士學(xué)位論文 前10條

1 包翔;基于尺度空間理論的自適應(yīng)圖像分割研究[D];江蘇大學(xué);2016年

2 杜俊楠;基于模糊聚類算法的圖像分割技術(shù)研究[D];魯東大學(xué);2015年

3 鄭福華;基于模糊C均值聚類的圖像分割算法研究[D];山東大學(xué);2014年

4 魏為;基于水平集方法的圖像分割技術(shù)研究[D];東北大學(xué);2014年

5 李銀松;基于遺傳算法的圖像分割方法[D];北京交通大學(xué);2014年

6 楊洋;基于模糊聚類理論的腦MR圖像分割算法的研究[D];中南大學(xué);2014年

7 徐京;基于水平集的醫(yī)學(xué)圖像分割算法研究[D];吉林大學(xué);2014年

8 李賜健;基于小波變換的遙感圖像分割方法研究[D];南昌航空大學(xué);2013年

9 宋轉(zhuǎn);基于水平集方法的圖像分割技術(shù)研究[D];浙江師范大學(xué);2013年

10 樊萬姝;基于半監(jiān)督模糊聚類的醫(yī)學(xué)圖像分割系統(tǒng)設(shè)計(jì)[D];大連理工大學(xué);2013年

，

本文編號：1743238