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研究生:黃耀仟
研究生(外文):Hwang, Yao-Chien
論文名稱:一個自動指骨影像切割系統之研製
論文名稱(外文):Design and Implementation of an Automatic Phalangeal Segmentation System
指導教授:鐘太郎
指導教授(外文):Jong, Tai-Lang
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:50
中文關鍵詞:指骨切割圓形除均法
外文關鍵詞:phalangeal segmentationround-average deduction
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  • 被引用被引用:2
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本篇論文在於發展出一套指骨ROI與指骨指節區域的前處理與切割系統,進而在未來增加特徵抽取與分析分類功能,發展成一套全自動化的指骨骨骼年齡、特徵判讀系統,從旁輔助醫生進行骨骼年齡的判讀。

在本篇論文中,我們提出一套全自動化的快速處理流程,可以用來切割出手掌X光照片中的指骨ROI與指骨指節區域。經由DICOM取得的手部X光照片影像經過了框選左掌,得到了本系統所需的輸入影像後,即可套入本系統的電腦自動判斷流程,然後完全不用下任指令、參數更動,即可從電腦自動判斷流程得到指骨與肉質、背景切割完成的結果。

於電腦自動化判斷切割過程中,程式主要做了三項工作:首先,利用眾數亮度值與三角形演算法,去除背景;接著再用質心為起點掃描方式,判別出手指位置且抽出;最後根據抽出的手指影像,利用梯形演算法、圓形除均演算法、以及Matlab填滿工具,將指骨從背景及肉質等不必要的區域當中切割出來。依照著以上的流程,對於拿到的40張X光照片影像與200張影像兩個群組,無分男女與年齡均可做統一作處理,最後統計,結果顯示,於一張圖片從讀入到處理完成只需要十幾秒的情形下,絕大部分的圖形可以成功切出完美的食指、中指、無名指的三個指節區域,並以圓形除均法切割出指骨骨頭。同時以5個衡量影像切割錯誤程度的指標來比較所提圓形除均法切割法與Adaptive Two-Means法切割結果,統計顯示圓形除均法在ME、RFAE、EMM以及NU的指標上,會比Adaptive Two-Means好一些,但是在MHD上則是表現較差,然而圓形除均法在計算時間上比Adaptive Two-Means則快速許多。

The thesis aims at developing an algorithm and its Matlab program to identify phalangeal ROI and to accurately segment the phanlangeal bone from the soft tissue and background in a left-hand X-ray image, so that the segmentation result can be utilized in subsequent feature extraction, analysis, and classification modules in a fully automatic computer assisted bone age assessment system. When inputting the cropped left-hand X-ray image from DICOM, the developed program can automatically process the image, remove the background to obtain the hand mask, locate the five phalangeal ROI’s, and segment the phanlangeal bone from the soft tissue and background of the distal, middle, and proximal phalanx without further human intervention and parameter adjustments.

Three major steps are employed in the developed program. First, the background portion of the left-hand X-ray image is removed by using the histogram mode with triangular algorithm to find a proper threshold to remove the background and to obtain the proper hand mask. Then using the centroid of the palm portion of the hand mask as a starting point with scan method, the locations of five fingers are determined and the corresponding phalangeal ROI’s are extracted. Finally, for each extracted distal, middle, and proximal ROI, the phalanx is segmented from tissue and other unnecessary background region by using a trapezoid algorithm, round-average deduction algorithms, and Matlab filling tool. 40 left hand X-ray images and 200 left hand images from more than 700 different subjects with ages covering 0 to 19 for both genders are processed and segmented by the proposed algorithm. The results statistics showed that, it only takes less than 10 seconds by the proposed Matlab program from reading-in a left-hand image to producing the segmented result, and most of the images can produce successfully extracted middle, distal, and proximal phalanx ROI’s, which are the three most important phalanx regions in clinical bone age research. Meanwhile, for comparison purpose, adaptive two-means segmentation algorithm is also implemented and the segmentation results of the proposed round-average deduction method and adaptive two-means on extracting phalanx bone region are evaluated by using five error measures: ME, RFAE, EMM, MHD, and NU. The experimental result statistics showed that, the proposed round-average deduction method performs better than adaptive two-means method on ME、RFAE、EMM and NU, but is worse on MHD. However, the round-average deduction method executes much faster than adaptive two-means.

摘要 ....................................................................................................................... i
Abstract ................................................................................................................... ii
致謝 ...................................................................................................................... iv
目錄 ...................................................................................................................... v
圖目錄 .................................................................................................................. vii
表目錄 .................................................................................................................. ix
第一章 簡介 ........................................................................................................ 1
1.1前言 ......................................................................................................... 1
1.2研究動機 ................................................................................................. 2
1.3研究目的 ................................................................................................. 2
1.4論文架構 ................................................................................................. 3
第二章 處理流程 ................................................................................................ 4
2.1切出左手 ................................................................................................. 4
2.2去除「背景」 .......................................................................................... 6
2.2.1去除「背景」步驟詳細說明 ....................................................... 8
2.2.2眾數、中位數、平滑filter ........................................................... 10
2.2.3三角形filter .................................................................................. 10
2.3判斷手指頭 ............................................................................................ 11
2.3.1判斷手指頭步驟 .......................................................................... 12
2.4 ROI切割與探討 ..................................................................................... 19
2.4.1 ROI切割步驟 .............................................................................. 20
2.4.2 大小filter ..................................................................................... 23
2.4.3 梯形filter ..................................................................................... 23
2.4.4 圓形除均filter ............................................................................. 25
2.4.5 Adaptive Two-Means filter ........................................................... 26
2.4.6 錯誤測量MOE(Measure of Errors) ......................................... 27
第三章 實驗結果 ................................................................................................. 30
3.1 取出手指區域 ..... .................................................................................. 30
3.2演算法的比較 ......................................................................................... 32
3.2.1判斷背景──「平均」、「眾數&三角形」、與「去除背景」 .. 32
3.2.2判斷關節點──「總和後微分」與「大小演算法」 ............... 33
3.2.3判斷骨頭──「圓形除均」與「Adaptive Two-Means」 ........ 35
第四章 結論 ........................................................................................................ 40
4.1結論 ......................................................................................................... 40
4.2未來方向................................................................................................... 41
參考文獻 .............................................................................................................. 42
附錄 ...................................................................................................................... 44

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