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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/63545


    Title: 基於先驗形狀主動輪廓模型及模糊分群之口腔磁振影像自動分割;Automatic Segmentation of Oral Magnetic Resonance Image Based on Shape Prior Active Contour Model and Fuzzy C-means Clustering
    Authors: 羅振洲;Lo,Chen-chou
    Contributors: 電機工程學系
    Keywords: 影像分割;磁振影像;等位函數法;梯度向量流蛇模型;先驗形狀;模糊分群;主動輪廓模型;Image Segmentation;MRI;Level Set Method;GVFS;Shape Prior;Fuzzy Clustering;Active Contour Model
    Date: 2014-01-27
    Issue Date: 2014-04-02 15:48:23 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 為了建構人體真實大小的舌頭模型以及了解舌頭肌肉分佈,本研究利用影像分割演算法來自動分割口腔磁振影像中舌頭的部分,再將舌頭二維影像建構為三維舌頭模型。為了改善先前研究對於口腔磁振影像的問題,本研究依據先前研究之建議於等位函數法中結合先驗形狀法與模糊分群法。每組個案之首張切面先經由模糊分群法處裡,使得影像中灰階對比增加進而讓等位函數演化較容易;非首張切面之初始輪廓則利用前張切面之輪廓結果來自動計算初始輪廓,並且透過先驗形狀等位函數法讓輪廓演化至目標邊界,最後利用梯度向量流蛇模型平滑化輪廓,達到全自動地對口腔磁振影像分割出舌頭構造。結果評估是將本研究結果與專家手動分割結果做比較,主要評估方法有百分比差、均方根差以及相似係數,其中以相似係數對於分割結果較靈敏,而八組個案結果之平均相似係數值為0.898,顯示本研究自動分割結果對於專家手動分割結果有很高的相似度,而分割結果之三維重建舌頭影像外型上與手動分割之結果亦大致相同。本研究成功地利用模糊分群法有效提升等位函數對於首張切面的分割結果以及利用前張分割結果作為先驗形狀,並且透過自動計算初始輪廓來提高等位函數法的分割結果。; In this study, we applied image segmentation algorithm to automatically segment the tongue contour from the oral magnetic resonance images (MRI) in order to construct a three dimensional (3-D) tongue in real human size and to study the anatomical structures of tongue muscles and reconstruct these 2-D slice results into a 3-D tongue. Based on the suggestion of the previous study from our laboratory, we adopted shape prior and fuzzy clustering knowledge into level set algorithm for solving the problems of previous research. We enhanced the pixel contrast of the first slice of each subject with fuzzy clustering to let level set contour evolve easier. For each non-first slice, we calculated the initial contour from the segmented tongue contour of the previous slice, and the segmented tongue contour of the previous slice also worked as the shape prior energy term to improve the current contour evolution. After contour evolutions, we used gradient vector flow snake to smooth the contour, and achieved automatic segmentation of oral MRIs. We evaluated the results of this study with the ground truth of tongue with the similarity index, percentage of difference and root mean square error. The similarity index is more sensitive to the accuracy of the segmented results among other evaluation methods, and the average similarity index of 8 subjects was 0.898 which indicated the similarity of the segmented results of this study is quite promising when compared to the ground truth, and the shape of the reconstructed 3-D tongue is similar to the one segmented with manual approach. This study used fuzzy clustering could improve the segmented results of level set for the first slice of each subject and the segmented tongue contour of the previous slice as a shape prior term successfully, and also calculated initial contour automatically to enhance the result of original level set method.
    Appears in Collections:[電機工程研究所] 博碩士論文

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