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使用形態學與模糊邏輯方法由全身電腦斷層掃描影像分割出肺部組織並找出裂隙 ...

國立高雄大學資訊工程學系碩士班

使用形態學與模糊邏輯方法由全身電腦斷層掃描影像分割出肺部組織並找出裂隙 = Lung Segmentation and Fissure Identification from the Whole Body CT Scans Using Morphology and Fuzzy Logic Methods

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	Lung Segmentation and Fissure Identification from the Whole Body CT Scans Using Morphology and Fuzzy Logic Methods
作者:	陳懋忠,
其他團體作者:	國立高雄大學
出版地:	[高雄市]
出版者:	撰者;
出版年:	民100
面頁冊數:	58葉圖，表格 : 30公分;
標題:	電腦斷層掃描
標題:	CT
電子資源:	http://handle.ncl.edu.tw/11296/ndltd/84812411061113116637
附註:	參考書目：葉46-48
摘要註:	利用X光技術完整顯現人體內部構造是令人驚奇的！如此，可以不需經過解剖來診斷人體是否患有疾病。由於電腦斷層掃描的圖片資料量是相當龐大，所以醫生常會靠電腦影像處理來輔助判讀。本論文主要研究為從各種不同病人的全身電腦斷層掃描資料先將全身影像分成頭、胸和腹部三群，再將胸部含有肺部組織的影像留下進而找出肺部裂隙。主要的方法是使用模糊邏輯推論(fuzzy logic)、多項式法(polynomial fit)與型態學中的邊緣偵測(edge detection)和邊界跟隨演算法(boundary following)並輔以經驗法則，來實現達成我們的目的。而模糊分類的整體正確性為96.4%，肺部組織分離的結果是完整且正確的，肺裂隙的平均均方差左肺為"1.4015㎜" ，右肺為"3.6364㎜" 。 It is amazing completely to show the internal structure of the human body by X-ray technology. Thus, by using we can diagnose whether the patient is sick. Doctors often rely on computer image processing to diagnosis because the number of the computed tomography scan is large. In this research, we firstly divide the whole body of the CT scans into three clusters: head, chest and abdomen. Next, we filter the images of the lung parenchyma of the chest and locate the lung fissures. The main approach is to use fuzzy logic inference, polynomial fit and the edge detection and boundary following of morphology and supplemented by experience rules to reach our goal. The overall fuzzy classification accuracy was 96.4% and the average standard deviation of lung fissures of left lung was "1.4015㎜" , right lung was "3.6364㎜" .

使用形態學與模糊邏輯方法由全身電腦斷層掃描影像分割出肺部組織並找出裂隙 = Lung Segmentation and Fissure Identification from the Whole Body CT Scans Using Morphology and Fuzzy Logic Methods
陳, 懋忠

使用形態學與模糊邏輯方法由全身電腦斷層掃描影像分割出肺部組織並找出裂隙 = Lung Segmentation and Fissure Identification from the Whole Body CT Scans Using Morphology and Fuzzy Logic Methods / 陳懋忠撰 - [高雄市] : 撰者, 民100. - 58葉 ; 圖，表格 ; 30公分.
參考書目：葉46-48.
電腦斷層掃描CT

使用形態學與模糊邏輯方法由全身電腦斷層掃描影像分割出肺部組織並找出裂隙 = Lung Segmentation and Fissure Identification from the Whole Body CT Scans Using Morphology and Fuzzy Logic Methods
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200 1 $a 使用形態學與模糊邏輯方法由全身電腦斷層掃描影像分割出肺部組織並找出裂隙 $d Lung Segmentation and Fissure Identification from the Whole Body CT Scans Using Morphology and Fuzzy Logic Methods $z eng $f 陳懋忠撰
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328 $a 碩士論文--國立高雄大學資訊工程學系碩士班
330 $a 利用X光技術完整顯現人體內部構造是令人驚奇的！如此，可以不需經過解剖來診斷人體是否患有疾病。由於電腦斷層掃描的圖片資料量是相當龐大，所以醫生常會靠電腦影像處理來輔助判讀。本論文主要研究為從各種不同病人的全身電腦斷層掃描資料先將全身影像分成頭、胸和腹部三群，再將胸部含有肺部組織的影像留下進而找出肺部裂隙。主要的方法是使用模糊邏輯推論(fuzzy logic)、多項式法(polynomial fit)與型態學中的邊緣偵測(edge detection)和邊界跟隨演算法(boundary following)並輔以經驗法則，來實現達成我們的目的。而模糊分類的整體正確性為96.4%，肺部組織分離的結果是完整且正確的，肺裂隙的平均均方差左肺為"1.4015㎜" ，右肺為"3.6364㎜" 。 It is amazing completely to show the internal structure of the human body by X-ray technology. Thus, by using we can diagnose whether the patient is sick. Doctors often rely on computer image processing to diagnosis because the number of the computed tomography scan is large. In this research, we firstly divide the whole body of the CT scans into three clusters: head, chest and abdomen. Next, we filter the images of the lung parenchyma of the chest and locate the lung fissures. The main approach is to use fuzzy logic inference, polynomial fit and the edge detection and boundary following of morphology and supplemented by experience rules to reach our goal. The overall fuzzy classification accuracy was 96.4% and the average standard deviation of lung fissures of left lung was "1.4015㎜" , right lung was "3.6364㎜" .
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610 1 $a CT $a Fuzzy logic $a Edge detection $a Boundary following $a Polynomial fit
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700 1 $a 陳 $b 懋忠 $4 撰 $3 518821
712 0 2 $a 國立高雄大學 $b 資訊工程學系碩士班 $3 353878
801 0 $a tw $b NUK $c 20111020 $g CCR
856 7 $z 電子資源 $2 http $u http://handle.ncl.edu.tw/11296/ndltd/84812411061113116637