本文選題：圖像分割 + 小波變換�。� 參考：《湘潭大學(xué)》2017年碩士論文

【摘要】：醫(yī)學(xué)圖像分割技術(shù)是醫(yī)學(xué)圖像處理的關(guān)鍵技術(shù),也是進(jìn)一步進(jìn)行圖像分析識別和各種醫(yī)學(xué)圖像應(yīng)用的基礎(chǔ)。在臨床診斷、輔助治療等方面圖像分割技術(shù)顯示出了越來越重要的臨床價值。從醫(yī)學(xué)圖像中自動分割出感興趣的目標(biāo)是件困難而艱巨的任務(wù),除了醫(yī)學(xué)圖像本身的復(fù)雜多樣性之外,其醫(yī)學(xué)圖像在成像過程中還會存在一定的噪聲,此外分割算法的結(jié)果也會受到部分場偏移效應(yīng)、局部體效應(yīng)、灰度不均勻性、偽影等因素的影響。傳統(tǒng)的分割方法顯然很難滿足醫(yī)學(xué)圖像分割的需要,所以對醫(yī)學(xué)圖像分割方法進(jìn)行深入的研究是非常必要的。小波變換在時域和頻域上都具有良好的局部檢測能力和多分辨率分析的特點,這是我們將小波變換應(yīng)用于細(xì)胞圖像分割的理論依據(jù)。傳統(tǒng)的邊緣檢測算子一般都是利用目標(biāo)邊緣的灰度不連續(xù)這一特性,通過計算出梯度局部極值像素點,連接這些像素點就是目標(biāo)邊緣,但是很容易受到噪聲信息的干擾。為此提出采用自適應(yīng)閾值的改進(jìn)的B樣條邊緣檢測算法,為了獲得精確的邊緣信息和除去噪聲的干擾,以二次B樣條函數(shù)為小波函數(shù),利用多孔算法,計算出局部模極大值點,再根據(jù)邊緣與噪聲的特征自動提出自適應(yīng)閾值,實現(xiàn)了噪聲與邊緣的分離,強邊緣與弱邊緣的分離,利用多尺度匹配融合策略,最終得到了綜合各個尺度精確的細(xì)胞圖像邊緣,并通過實驗分析驗證了算法的有效性。醫(yī)學(xué)圖像處理提取細(xì)胞中使用分水嶺方法時,容易產(chǎn)生過分割現(xiàn)象且對噪聲的干擾極為敏感,為了解決此缺點,提出一種基于小波變換和形態(tài)學(xué)分水嶺的細(xì)胞圖像分割新方法。改算法利用小波變換多分辨率分析對圖像進(jìn)行分解,選取合適的小波基和改進(jìn)去噪閾值函數(shù)對圖像進(jìn)行小波去噪,然后對去噪后小波重構(gòu)的細(xì)胞圖像應(yīng)用數(shù)學(xué)形態(tài)學(xué)距離變換、灰度重建等技術(shù)產(chǎn)生的區(qū)域標(biāo)記進(jìn)行分水嶺變換,最終得到分割結(jié)果。實驗結(jié)果表明,該算法能穩(wěn)定、準(zhǔn)確地提取細(xì)胞和實現(xiàn)粘連細(xì)胞的自動分割,同時具有很好的魯棒性和普適性。
[Abstract]:Medical image segmentation is the key technology of medical image processing, and it is also the basis of image analysis and recognition and various medical image applications. Image segmentation technology has shown more and more important clinical value in clinical diagnosis, adjuvant therapy and so on. Automatic segmentation of objects of interest from medical images is a difficult and difficult task. In addition to the complexity and diversity of medical images, there is still some noise in the imaging process. In addition, the results of the segmentation algorithm are also affected by some factors, such as partial field migration effect, local volume effect, gray inhomogeneity, artifact and so on. The traditional segmentation method is obviously difficult to meet the needs of medical image segmentation, so it is very necessary to deeply study the medical image segmentation method. Wavelet transform has good local detection ability and multi-resolution analysis in both time and frequency domain, which is the theoretical basis for the application of wavelet transform to cell image segmentation. The traditional edge detection operator usually utilizes the discontinuity of gray level of the edge of the target. By calculating the gradient local extremum pixels, connecting these pixels is the edge of the target, but it is easy to be disturbed by the noise information. An improved B-spline edge detection algorithm based on adaptive threshold is proposed. In order to obtain accurate edge information and remove noise, the quadratic B-spline function is used as wavelet function, and the local modulus maximum point is calculated by using porous algorithm. Then according to the feature of edge and noise, the adaptive threshold is put forward automatically, the separation of noise and edge, the separation of strong edge and weak edge, and the multi-scale matching fusion strategy are used to obtain the cell image edge which synthesizes all scales accurately. The validity of the algorithm is verified by experimental analysis. In order to solve this problem, a new method of cell image segmentation based on wavelet transform and morphological watershed is proposed. The modified algorithm decomposes the image by wavelet transform multi-resolution analysis, selects the suitable wavelet basis and the improved denoising threshold function to carry on the wavelet de-noising to the image, and then applies the mathematical morphological distance transform to the cell image reconstructed by the de-noising wavelet. Finally, the segmentation results are obtained by watershed transformation of the regional markers generated by gray level reconstruction. Experimental results show that the proposed algorithm can extract cells accurately and achieve automatic segmentation of adherent cells, and has good robustness and universality.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R318;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陳建祥;靜大海;;基于鏈碼的分水嶺和區(qū)域合并算法[J];國外電子測量技術(shù);2016年12期

2 邰瀅瀅;吳彥海;張利;;基于快速mean-shift聚類與標(biāo)記分水嶺的圖像分割方法[J];計算機應(yīng)用與軟件;2015年08期

3 陳潔;胡永;劉澤國;;基于標(biāo)記的分水嶺圖像分割算法研究[J];軟件;2012年09期

4 胡凱;張穎超;錢蕾;;最優(yōu)小波尺度空間的圖像邊緣檢測方法[J];南京信息工程大學(xué)學(xué)報(自然科學(xué)版);2011年03期

5 刁智華;趙春江;郭新宇;陸聲鏈;王秀徽;;分水嶺算法的改進(jìn)方法研究[J];計算機工程;2010年17期

6 楊治明;王曉蓉;彭軍;陳應(yīng)祖;;BP人工神經(jīng)網(wǎng)絡(luò)在圖像分割中的應(yīng)用[J];計算機科學(xué);2007年03期

7 張永賢;陳梅;;一種閾值自動設(shè)定的小波變換邊緣檢測方法[J];微計算機信息;2007年03期

8 叢培盛;孫建忠;;分水嶺算法分割顯微圖像中重疊細(xì)胞[J];中國圖象圖形學(xué)報;2006年12期

9 勞麗;吳效明;朱學(xué)峰;;模糊集理論在圖像分割中的應(yīng)用綜述[J];中國體視學(xué)與圖像分析;2006年03期

10 郭斯羽;張煦芳;;一種基于模型的自適應(yīng)閾值分割算法[J];浙江大學(xué)學(xué)報(工學(xué)版);2005年12期

相關(guān)博士學(xué)位論文 前1條

1 梁光明;體液細(xì)胞圖像有形成分智能識別關(guān)鍵技術(shù)研究[D];國防科學(xué)技術(shù)大學(xué);2008年

相關(guān)碩士學(xué)位論文 前1條

1 饒潔;基于區(qū)域特征及邊緣信息的彩色血液顯微圖像分割[D];四川大學(xué);2006年

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