北京生物醫(yī)學(xué)工程

一種改進(jìn)局部二元擬合的灰度非均勻圖像分割

An Improved Local Binary Fitting Model For Non-Uniform Image Segmentation

作者：田飛楊豐

單位：南方醫(yī)科大學(xué)生物醫(yī)學(xué)工程學(xué)院(廣州510515)

分類號(hào)：

出版年·卷·期（頁(yè)碼）：2010·29·3（225-229）

摘要：

針對(duì)局部二元擬合(local binary fitting，LBF)能量模型對(duì)活動(dòng)輪廓曲線初始位置較為敏感的缺點(diǎn)，本文提出一種改進(jìn)局部二元擬合的灰度非均勻圖像分割模型。首先把水平集函數(shù)初始化為一個(gè)常數(shù)，然后在迭代過程中引入一個(gè)擾動(dòng)項(xiàng)，從而引導(dǎo)目標(biāo)區(qū)域的水平集函數(shù)值發(fā)生符號(hào)變化。實(shí)驗(yàn)結(jié)果表明，上述模型能夠有效地應(yīng)用于灰度非均勻圖像的分割。與LBF模型相比，本文模型無(wú)需人工選擇活動(dòng)輪廓曲線的初始位置，且避免了由于初始位置選擇不當(dāng)造成的分割錯(cuò)誤。

In order to overcome the shortage of Local Binary Fitting model’s sensitivity to initial contour position, this paper develops an improved local binary fitting(LBF)model for non-uniform image segmentation. Firstly the level set function is initialized to a constant and then the disturbance term is introduced into the iteration, making the sign of function value in object area change from negative to positive. Experiments show that our proposed method is able to work effectively on segmentation of non-uniform images. Compared with LBF method, our improved LBF method does not need to artificially select the initial position of active contour curves and avoids the segmentation error caused by improper choice of initial contour position.

參考文獻(xiàn)：

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