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    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/77793

    題名: 骨折區檢測;Fracture Region Detection
    作者: 歐莉亞;Auliya′, Nurotul
    貢獻者: 資訊工程學系
    關鍵詞: CT scan images;calcaneus bone fracture;detection;segmentation;CT scan images;calcaneus bone fracture;detection;segmentation
    日期: 2018-08-23
    上傳時間: 2018-08-31 14:56:39 (UTC+8)
    出版者: 國立中央大學
    摘要: 跟骨是人體足中最大骨,最重要的骨骼。它的作用是保持人體重量和其施加的力量。然而,這種骨骼也是最常受傷的骨。在大多數情況下,發生損傷由於過度的軸向載荷。
    ;Calcaneus bone is the biggest and the most important bone in human foot. Its role is to hold human weight and other forces that they applied to it. However, this bone is also the most frequently injured tarsal bone. In most cases, the injuries happen because of excessive axial load such as a fall from height or in a motor vehicle accident.
    Calcaneus bone fracture classification is used to classify the injured calcaneus bone into types with each type needing a different kind of treatment. Among the several different classification systems, Sanders classification is the most commonly used because it is based on the coronal computed tomography (CT) scan of the calcaneal fracture that shows the widest undersurface of the posterior facet of the calcaneus. However, in spite of its popularity, there is a high degree of variability and inconsistency in its interpretation with only fair to moderate consistency among its users, which is mostly caused by the difference in experience. Such inconsistency is significant because a mistake in treatment may cause irreparable harm to the patient.
    Currently, doctors manually examine the chosen CT scan image, which contains the most complete calcaneus structure, then classify it according to calcaneus fracture classification. This is prone to mistakes, not only because of the aforementioned problem but also because sometimes the CT scan image is blurred and does not show clear fracture line.
    This thesis attempts to help in identifying the calcaneus fracture type by detecting calcaneus bone fragments and showing the fracture lines.
    The first part is to differentiate each bone fragment from the other bone fragments in the image. The second part is to point out the calcaneus bone fragments and show their edges. This system successfully automatically determine which fragments belong to calcaneus bone.
    顯示於類別:[資訊工程研究所] 博碩士論文


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