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研究生:張吉興
研究生(外文):Chi-Hsing Chang
論文名稱:利用指骨特徵進行電腦化骨骼年齡自動判讀之研究
論文名稱(外文):An Investigation of Computerized Automatic Bone Age Assessment System Based on Phalangeal Image Features
指導教授:鐘太郎
指導教授(外文):Tai-Lang Jong
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:2003
畢業學年度:91
語文別:英文
論文頁數:54
中文關鍵詞:骨骼年齡特徵抽取生理特徵形態特徵指骨
外文關鍵詞:bone agefeature extractionphysiological featuremorphological featurephalanx
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骨骼年齡的判讀在小兒放射科上有極大的應用,它通常被用來判斷病人是否有生長失序或內分泌失調的問題,也可用來判斷一般人身高發展的潛力。如果骨骼年齡和實際年齡不相符,表示病人發育不正常。因此研究電腦化骨骼年齡自動判讀系統是很有用的。
在本篇論文中,我們提出一套全自動化的流程,可以用來判讀手掌X光影像中的指骨骨骼年齡。在前處理中,我們主要做了三項工作。首先,我們利用簡單的掃描方式切割出左手X光影像,接著再利用另一種掃描方式得到一張投影圖,根據這張投影圖來定義並找出中指指骨所屬的區域,最後是使用 Gabor Filter,Canny Edge Detector,以及 Local Variance Edge Detector等三種工具,將指骨從背景及肉質等不必要的區域當中切割出來。為了判讀指骨骨骼年齡,我們從切割出來的影像中抽取出指骨生理特徵和指骨關節的形態徵,再分別將這兩組特徵依序利用三種不同的類神經網路來進行骨骼年齡判讀。此外,我們也統計了各個特徵在不同骨骼年齡的分布狀況,由此可以判斷出特徵和指骨骨骼年齡的關係。最後,我們統計了各組特徵用來判讀骨骼年齡的成功率,在誤差一歲半的容許範圍以內,正確率約略可以達到八成五左右。
本篇論文的目的在於發展出一套全自動化的指骨骨骼年齡判讀流程,進而在未來發展成一套全自動化的指骨骨骼年齡判讀系統,從旁輔助醫生進行骨骼年齡的判讀。

This paper describes a fully automatic computerized bone age assessment procedure based on phalangeal ossification features. Once a hand radiograph is read in, the bone age will be judged without any manual assistance. In the presented procedure, first a preprocessing stage, including finding the position of the left hand, defining and detecting phalangeal bone region of interest (PROI), and segmentation of the phalange by using Gabor filter, Canny edge detector, and local variance edge detector, is performed. Then two different sets of features, physiological and morphological features, are extracted from the segmented images to make bone age assessments using several kinds of neural networks. The effectiveness of the two sets of features is analyzed and the correct rates of the two sets are computed and compared. It is concluded that the morphological features perform better than the physiological features due to its ability to describe the development of bones. The best correct rates of assessed bone ages within 0.5 years errors, 1.0 year errors, 1.5 years errors, and 2.0 years errors, are 49.21%, 71.91%, 86.29%, and 93.25% in female, and 42.74%, 67.81%, 84.17%, and 91.02% in male, by virtue of leave-one-out cross validation, respectively. In a clinical aspect, errors within 1.5 years are acceptable and thus the system is satisfactory.

Abstract ..........................................................................................................................I
Contents ........................................................................................................................II
Chapter 1 Introduction ..................................................................................................1
Chapter 2 Preprocessing ................................................................................................4
2.1 Crop the Left Hand ..........................................................................................5
2.2 Rotation & Finding Phalangeal Bone Region of Interest ................................7
2.3 Segmentation of Phalangeal Bone Region of Interest ...................................14
2.3.1 Enhancement ......................................................................................14
2.3.1.1 Smoothing ...............................................................................14
2.3.1.1.1 Gaussian Filter ..............................................................14
2.3.1.1.2 Gabor Filter ..................................................................15
2.3.1.2 Edge Detecting ........................................................................16
2.3.1.3 Local Variance Edge Detector .................................................17
2.3.2 Procedure of Segmentation of Phalanxes ...........................................17
Chapter 3 Feature Extraction .......................................................................................21
3.1 Physiological Features ...................................................................................21
3.2 Morphological Features .................................................................................23
Chapter 4 Experimental Results ..................................................................................27
4.1 Evaluation of Accuracy of Preprocessing .....................................................27
4.2 Discrimination Power of Extracted Features ................................................30
4.3 Comparison of Correct Rates of Bone Age Assessment with Different
Feature Sets Using Neural Networks ............................................................38
Chapter 5 Conclusions ................................................................................................43
5.1 Conclusion ....................................................................................................43
5.2 Future Work ..................................................................................................44
Appendix: Neural Network .........................................................................................45
A.1 Back-Propagation Multilayer Perceptrons ...................................................46
A.2 Support Vector Machines .............................................................................50
Reference ....................................................................................................................53

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