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虹膜病灶特徵辨識方法

A Method For Identifying Nidus In The Iridology

Advisor : 劉 啟 東
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Abstracts


虹膜學發展已經超過100 年,虹膜可反應出個人的身體的健康狀 況與疾病,本篇論文運用影像處理的演算法,來鑑定與辨別虹膜影像 的標記資訊,使用Canny 邊緣偵測運算子來定位病灶上局部的邊緣, 並運用曲線擬合的方法來確認橢圓形的病灶,實驗的結果顯示出能夠 有效的確認出虹膜上的樣本,經過測試的影像共14 張,5 張具有病 灶特徵、另外9 張則不具有病灶特徵,正確辨識率為85.2%、錯誤辨 識率為29.4%,本論文主要目的在於偵測出虹膜上的開放與封閉式病 灶。

Parallel abstracts


Iridology has developed more than 100 years, the iris can reflect a person''s state of health and systemic disease. In this paper we present a method to identify and discriminate the signature information in iris images. We use Canny edge detector to locate the partial edge of lacuna. We then use curve fitting method to identify the elliptic lacuna. The experimental results show that we can identify iris pattern automatically and efficiently. The proposed method had tested on 14 images, 5 of them contain lacuna area and 9 contain false lacuna area. We had achieved identification rate 85.2% and miss identification rate 29.4%. The goal of this paper is to detect the location of open or closed patterns in iris.

Parallel keywords

Ellipse fitting Edge detection Iridology

References


[17] 孫嘉陽,基於動態輪廓模型之移動目標物即時偵測與追蹤研究,碩士論文,國立成功大學機械工程學系,台南,2004。
[1] A.D.Wibawa and M.H.Purnomo, ”Early Detection on the Condition of Pancreas Organ as the Cause of Diabetes Mellitus by Real Time Iris Image Processing,” IEEE Asia Pacific Conference on Circuits and Systems, 2006.
[5] B. Jensen, Iridology Simplificated. fifth edition, 1980.
[7] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing Prentice-Hall, Second Edition, 2002.
[8] J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, Vol. 8, No.6, pp. 679-698.

Cited by


周毅帆(2013)。虹膜檢測系統與瞳孔收縮率之研究〔碩士論文,朝陽科技大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0078-2712201314042333

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