電腦輔助系統應用於肝臟分割影像分析

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Title:	電腦輔助系統應用於肝臟分割影像分析 Computer-assisted Image Analysis System for Liver Segmentation Using Abdominal Computed Tomography
Authors:	蔣獻文;Hsien-Wen Chiang
Contributors:	醫學院臨床醫學研究所碩士班
Keywords:	電腦輔助偵測系統;肝臟體積;影像分割;computer-aided detection system;liver volume;image segmentation
Date:	2010
Issue Date:	2010-09-29 12:16:55 (UTC+8)
Abstract:	醫學影像能提供臨床或外科醫師在疾病的診斷過程或手術前的評估，提供一個重要的資訊，搭配電腦輔助系統(computer-aided diagnosis﹐CAD)的應用，可以快速從原始的醫學影像中獲取資訊。本研究的目的在於建立一套針對肝臟影像分割的自動化電腦輔助系統，應用於肝臟容積的建立。現今肝臟容積的建立必須藉由人工手動圈選的方式，由於此方式非常的耗時，所以我們希望藉由此電腦輔助系統的建立，可以輔助醫師快速的獲得肝臟的容積比率，所使用的影像來源為電腦斷層影像，在肝臟醫學影像中，由於不同的年齡、體型、性別等會有差異性存在，但這些肝臟的影像又有著相似的灰度值範圍，我們建立一套初步肝臟邊緣提取方法，我們的方法可以分成3個部份, 1. 強化骨頭部份並去除, 2. 強化肝臟影像確定邊緣, 3. 使用區域生長法(region growing)在肝臟部份導入種子點使之聚集，將肝最大的面積相連接，找出完整的肝臟輪廓，計算出肝臟的容積比率。最後我們以平均準確率以TPF. FPF. 準確率. 錯誤率表示做為評估標準，其結果為93. 2±2%、99. 02±0. 24%、98. 8±0. 25%、1. 12±0. 25%。 Medical imaging can provide important information for clinical or surgical physicians in the diagnosis of the disease process or pre-operative assessment with computer-aided system (computer-aided diagnosis, CAD) application. It is able to interpret a medical image to data information. The purpose of this study is to establish a semi-automatic computer-aided detection system for liver segmentation for liver volume measurement. The current liver volume segmentation is performed manually by physicians in our hospital (Chang Gung Memorial Hospital). It is time-consuming and the result is dependent on experiences which is hardly repeatable. Based on the developed computer-aided detection system, clinicians are able to access the ratio of liver volume more efficiently. We use CT images to perform live volume measurement. However, there are variations between ages, body sizes, and genders, which make different representations in CT images. Luckly the range of Hounsfield unit values in these liver images are similar, which are useful information applied in our system. The scheme can be divided into three parts: 1. Strengthen and remove the bone part. 2. Strengthen the determined edge of liver image. 3. Use region growing to group liver pixels so that the liver area can be obtained. Find the complete liver contour. Calculate the liver volume ratio. Finally, we use the TPF FPF accuracy rate, and error rate to be the evaluation. The results are 93. 2 ± 2%, 99. 02 ± 0. 24%, 98. 8 ± 0. 25%, 1. 12 ± 0. 25%, respectively.
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