| DC 欄位 |
值 |
語言 |
| DC.contributor | 數學系 | zh_TW |
| DC.creator | 阮毓庭 | zh_TW |
| DC.creator | Yu-Ting Juan | en_US |
| dc.date.accessioned | 2019-8-16T07:39:07Z | |
| dc.date.available | 2019-8-16T07:39:07Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=106221005 | |
| dc.contributor.department | 數學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 肝臟是人體的重要器官，負責許多生理所需功能且肝病一直是台灣十大死因之一。早期原發性肝癌很難被發現，因為最初的症狀通常不明顯。想要控制肝癌，除非在腫瘤非常小的情況下就已經發現，否則將難以控制病情。因此，我們希望建立肝臟結構的數值模擬，包括血管和肝臟結構。在模擬之前，我們需先將肝臟從影像中分割出來。
醫學圖像通常包含複雜的訊息，圖像分割也是許多醫學應用中的挑戰。精確切割對於模擬是有必要的。但尋找照MRI的受試者並不是一件簡單的事情，所以在各方考量下，我們使用小老鼠的肝影像進行模擬。然而，MR影像中的小鼠肝臟邊界通常不清楚，傳統edge-based切割方法並不合適。在本文中，我們提出了一種方法，利用MRI的T1，T2和T1 C+(Primovist)的成像差異先將不需要的組織器官移除並得到一個新的影像，再使用k-means方法進行分割，結果顯示準確度確實有所改善。未來，我們期望能實際應用在人體的數值模擬。 | zh_TW |
| dc.description.abstract | Liver diseases are always on the list of the top 10 causes of death in Taiwan. Early primary liver cancer is difficult to detect because the initial symptoms are usually not obvious. But unless it is discovered when the tumor is very small, liver cancer is difficult to control. therefore, we desire to build a numerical simulation of the liver structure, including blood vessel topography, liver surface. Before the simulation, we should segment liver from MR images.
Medical images mostly contain complicated structures, and image segmentation is a key task in many medical applications. Their precise segmentation is necessary for simulation. Since seeking the subject for scanning MRI isn′t a simple matter, we use a mouse liver image to do simulation. However, mouse liver boundaries in MR images are usually unclear, the traditional edge-based method for segmentation is unsuitable. In this paper, we propose a way that creating a new image is combined T1-weighted (T1), T2-weighted MRI (T2) and T1-weighted MRI with contrast enhancement (T1 C+(Primovist)) image. We compare the image which doing confusion component removing with the original image after segmentation using k-means method afterward. The result presents that accuracy is improved. In the future, we look forward to applying on the numerical simulation.
| en_US |
| DC.subject | 核磁共振 | zh_TW |
| DC.subject | 影像處理 | zh_TW |
| DC.subject | k-means | en_US |
| DC.subject | MRI | en_US |
| DC.title | Three-dimensional Geometry Reconstruction of Mouse Liver from MR Images Using K-means Method with Confusion Component Removing | en_US |
| dc.language.iso | en_US | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |