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使用形態學、影像分割與K-means方法由胸部斷層掃描圖自動產生胸部精簡...

國立高雄大學資訊工程學系碩士班

使用形態學、影像分割與K-means方法由胸部斷層掃描圖自動產生胸部精簡地圖 = Automatically Generate a Simplified Chest Atlas from the Chest Computed Tomography Using Morphology, Image Segmentation and K-means

Record Type:	Language materials, printed : monographic
Paralel Title:	Automatically Generate a Simplified Chest Atlas from the Chest Computed Tomography Using Morphology, Image Segmentation and K-means
Author:	蔡岳洋,
Secondary Intellectual Responsibility:	國立高雄大學
Place of Publication:	[高雄市]
Published:	撰者;
Year of Publication:	2009[民98]
Description:	70面圖、表 : 30公分;
Subject:	K-means
Subject:	Erosion
Online resource:	http://handle.ncl.edu.tw/11296/ndltd/58013237622782816627
Notes:	參考書目：面
Notes:	指導教授：殷堂凱
Summary:	隋著科技的進步，電腦可以為我們人類所做的事情也越來越多。不論是生活、娛樂工作方面都帶給我們許多方便。甚至是關於人體健康的醫療方面，也有針對不同需要所開發的軟體給醫生作判讀使用。本論文針對不同病人間胸部斷層掃描圖去做解剖學上部位的標示。由於拍攝斷層掃描圖都會包含到我們做處理不希望留下的平台部份，所以先是利用類似斷開的技術也就是連續的侵蝕加上連續的膨脹將我們不需要的平台跟一些雜訊部份去除掉，之後使用Sobel邊緣偵測將此影像的大概重要輪廓取出來，針對不同的斷層掃描圖利用Otsu's方法自動設定不同的灰階度的臨界值將灰階值較低的肌肉跟臟器部分去除掉，留下部分腔室與骨骼的輪廓做K-means演算法作分群並將每一群的中心點依解剖學標示出所位在的部位。最終其結果，我們所取的部位點,跟我們所想要標示的位置，在我們實驗的所有病人最好的情況以像素為單位可以達到總平均10.5582的歐幾里得距離。 With the improvement of technology, computers have given human beings a lot of improvement. These improvements include in our lifestyles and entertainment. Especially in the medical field, computers have given doctors a much more accurate diagnosis of patients. This paper deals with different patient’s chest CT (Computerized Tomography), which will give an analysis according to the human anatomy. When CTs are taken there will be outside influences, for example platforms will be photographed as well. Using a method similar to the method of opening which is a recursive erosion and dilation techniques, this would allow outside influences to be eliminated. After this, Sobel edge detection is used to get the outline of the pictures. Use Otsu’s method to automatically get the gray level threshold, and eliminate muscles or organs which have a lower gray level. This is due to that muscles and organs have lower gray levels. This would leave cavities and bone cavities to appear on the CT. Finally, use K-Means to cluster. Each cluster will be a center point. Thus, the center points will give a clear view of the locations of all parts. In conclusion, the mean of total Euclidean distances between the labeled body parts and the ideal body positions is 10.5582 pixels.

使用形態學、影像分割與K-means方法由胸部斷層掃描圖自動產生胸部精簡地圖 = Automatically Generate a Simplified Chest Atlas from the Chest Computed Tomography Using Morphology, Image Segmentation and K-means
蔡, 岳洋

使用形態學、影像分割與K-means方法由胸部斷層掃描圖自動產生胸部精簡地圖 = Automatically Generate a Simplified Chest Atlas from the Chest Computed Tomography Using Morphology, Image Segmentation and K-means / 蔡岳洋撰 - [高雄市] : 撰者, 2009[民98]. - 70面 ; 圖、表 ; 30公分.
參考書目：面指導教授：殷堂凱.
K-meansErosion

使用形態學、影像分割與K-means方法由胸部斷層掃描圖自動產生胸部精簡地圖 = Automatically Generate a Simplified Chest Atlas from the Chest Computed Tomography Using Morphology, Image Segmentation and K-means
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330 $a 隋著科技的進步，電腦可以為我們人類所做的事情也越來越多。不論是生活、娛樂工作方面都帶給我們許多方便。甚至是關於人體健康的醫療方面，也有針對不同需要所開發的軟體給醫生作判讀使用。本論文針對不同病人間胸部斷層掃描圖去做解剖學上部位的標示。由於拍攝斷層掃描圖都會包含到我們做處理不希望留下的平台部份，所以先是利用類似斷開的技術也就是連續的侵蝕加上連續的膨脹將我們不需要的平台跟一些雜訊部份去除掉，之後使用Sobel邊緣偵測將此影像的大概重要輪廓取出來，針對不同的斷層掃描圖利用Otsu's方法自動設定不同的灰階度的臨界值將灰階值較低的肌肉跟臟器部分去除掉，留下部分腔室與骨骼的輪廓做K-means演算法作分群並將每一群的中心點依解剖學標示出所位在的部位。最終其結果，我們所取的部位點,跟我們所想要標示的位置，在我們實驗的所有病人最好的情況以像素為單位可以達到總平均10.5582的歐幾里得距離。 With the improvement of technology, computers have given human beings a lot of improvement. These improvements include in our lifestyles and entertainment. Especially in the medical field, computers have given doctors a much more accurate diagnosis of patients. This paper deals with different patient’s chest CT (Computerized Tomography), which will give an analysis according to the human anatomy. When CTs are taken there will be outside influences, for example platforms will be photographed as well. Using a method similar to the method of opening which is a recursive erosion and dilation techniques, this would allow outside influences to be eliminated. After this, Sobel edge detection is used to get the outline of the pictures. Use Otsu’s method to automatically get the gray level threshold, and eliminate muscles or organs which have a lower gray level. This is due to that muscles and organs have lower gray levels. This would leave cavities and bone cavities to appear on the CT. Finally, use K-Means to cluster. Each cluster will be a center point. Thus, the center points will give a clear view of the locations of all parts. In conclusion, the mean of total Euclidean distances between the labeled body parts and the ideal body positions is 10.5582 pixels.
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