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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/508


    Title: 應用自適應閾值於區域成長法之顱內血腫影像切割;Intracranial hemorrhage Segmentation in Computed Tomography Images Using Region Growing Algorithm with Adaptive Thresholding
    Authors: 黃巧文;HUANG, CHIAO-WEN
    Contributors: 資訊管理系研究所
    Keywords: 自發性顱內出血;電腦斷層掃描;影像切割;自適應閾值;區域成長法;ICH;CT;Image Segmentation;Adaptive Thresholding;Region Growing
    Date: 2017
    Issue Date: 2019-07-17
    Publisher: 資訊管理系研究所
    Abstract: 根據我國衛生福利部統計指出,腦血管疾病高居國人十大死因的第三名。而自發性顱內出血,亦即出血性腦中風,屬於腦血管疾病的一種,可能會造成半身不遂、語言困難、意識不清,甚至昏迷死亡,其死亡率更比缺血性腦中風要來得高。電腦斷層掃描為目前臨床上診斷自發性顱內出血的第一線檢查工具,具有能準確地區分中風類型、出血位置及血塊大小等等的特性,且出血在電腦斷層掃描中呈現高密度,較容易被辨識。出血位置與出血體積是醫師決定是否手術之依據,而手術目的主要是減少腦內出血所產生的併發症;目前臨床上測量顱內出血大小的方法是以電腦斷層掃描影像上血塊之長寬高來估計。但由於血塊多呈現不規則的形狀,以長寬高作為評估指標並不能準確地量測血塊的實際大小。因此,本研究提出一套自動化影像切割流程,希望藉此作為醫生診斷之參考依據,並減少圈選血腫的時間。我們將自適應閾值與區域成長法相結合,透過大津算法動態取得之門檻值作為決定種子點強度值之依據,即可對多個初始種子點進行區域成長,實現自動化切割的目的。實驗結果顯示,本研究圈選之區域與醫生手動圈選之輪廓有著極高的契合度,且擁有良好的準確率及特異度。
    According to statistics of government, cerebrovascular diseases rank number three among the top ten leading causes of death in Taiwan. Intracranial hemorrhage, or hemorrhagic stroke, is a kind of cerebrovascular diseases. It may lead to hemiplegia, language disorder, unconsciousness, and even death. Moreover, the mortality rate of hemorrhagic stroke is higher than the rate of ischemic stroke.Computed tomography is the main imaging modality used for diagnosis and evaluation of intracranial hemorrhage, which can distinguish the type of stroke and the location and the size of the hematoma. Because hematoma is of high density in computed tomography, it is easy to identify. The location and the volume of the hematoma are the basis for clinicians to decide whether to operate. Due to the irregular shape of hematoma, the volume of hematoma cannot measure accurately if only use its length, width and height to calculate. Therefore, we propose an automatic image processing method that aims to provide a powerful tool for clinicians to evaluate the intracranial hemorrhage in computed tomography, and to reduce the time of manual selection. Combining adaptive thresholding and region growing, we perform otsu’s method to find initial seeds automatically. Through this method, we can determine the gray value of the seed point by dynamic threshold obtained from Otsu’s method. The results show that the region circled by this study and the contour manually circled by the doctor have great similarity, and yield good accuracy and specificity.
    Appears in Collections:[資訊管理系研究所] 學位論文

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