北京生物醫(yī)學(xué)工程

基于紅外圖像的手關(guān)節(jié)區(qū)域自動提取

Automated extraction of metacarpophalangeal joints in infrared images of human hands

作者：秦暢卞春華 Monique Frize

單位：南京大學(xué)電子科學(xué)與工程學(xué)院(南京 210093)

關(guān)鍵詞：興趣區(qū)域；紅外圖像；人手；定位；掌指關(guān)節(jié)

分類號：

出版年·卷·期（頁碼）：2012·31·2（130-133）

摘要：

目的手工選取關(guān)節(jié)區(qū)域的診斷方式增加了關(guān)節(jié)類疾病診斷的主觀性和工作量。針對這一問題本文提出一種自動定位紅外圖像中人手關(guān)節(jié)的方法。方法根據(jù)人手的解剖學(xué)結(jié)構(gòu)和手部特征點(diǎn)的位置，提出自動提取手關(guān)節(jié)區(qū)域的3種模型，即四邊形模型、橢圓形模型和圓形模型，其中手部特征點(diǎn)包括目前手部圖像研究中常用的11個特征點(diǎn)，以及本文新提出的2個輔助特征點(diǎn)。最后，通過一種基準(zhǔn)圖像比較方法驗(yàn)證本文方法的有效性。結(jié)果使用該方法能夠準(zhǔn)確找到關(guān)節(jié)中心的位置，在紅外圖像中自動定位出手部關(guān)節(jié)區(qū)域。結(jié)論該關(guān)節(jié)區(qū)域自動提取算法有助于關(guān)節(jié)定位及疾病診斷。

Objective At present,joint areas still need to be located manually in the diagnosis of joint diseases,which is subjective and time consuming.To solve this problem,this paper presents a method for automated extraction of metacarpophalangeal (MCP) joints in infrared images of human hands.Methods Based on the averaged anatomical structure and the 13 landmarks of hand,three models：rectangle model,ellipse model and circle model are proposed.The 13 landmarks used in this paper include 11 common accepted landmarks in hand image research and two assistant landmarks proposed here.In addition,a new method of comparing with reference images is proposed to evaluate the accuracy and validity of the three models.Results The algorithm can help us to locate the centers of the joints and extract the joint areas accurately in infrared images.Conclusions The algorithm assists the localization of hand joints and is helpful to the diagnosis and therapy of joint diseases.

參考文獻(xiàn)：

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