在居家傷口看護中，病人壓瘡傷口的日常照顧通常由主要照顧者負責，而護理人員則需一段時間定期去做壓瘡傷口紀錄。而隨著影像辨識與深度學習技術的快速發展，近年來許多醫學上的研究提出影像辨識技術去解決問題。因此本論文希望能結合影像辨識與深度學習技術，輔助傷口記錄人員在對傷口患者紀錄傷口時，減少患者露出傷口的時間，並提供一些有用的傷口資訊輔助人員分析。
本論文的目的為設計一套傷口資訊辨識系統(Wound information identification system, WIIS)，針對開放式且相連接的慢性傷口，只要讓傷口跟提供的比例色塊一起拍照後，就能透過傷口的影像辨識出其傷口資訊，提供輔助傷口記錄人員的傷口資訊如傷口面積、傷口顏色比例分布等，來達到輔助傷口紀錄者，減少人員的負擔。
本論文提出基於深度學習的傷口輪廓檢測模型WcpCNN(Wound contour positioning based on CNN)與WcpMLP(Wound contour positioning based on MLP)，利用WcpCNN與WcpMLP檢測其輪廓，跟傳統Canny相比，WcpMLP傷口面積誤差最低。並根據實驗歸納出當相機鏡頭與傷口保持平行時，旋轉不會造成誤差，但當相機角度傾斜時，角度越大，旋轉影響的效能就會越大。

At home wound care, the daily care of pressure ulcers is usually the responsibility of the primary caregiver, and the nursing staff need to make regular pressure ulcer records. With the rapid development of image recognition and AI technology, many medical researches in recent years have proposed image recognition technology to solve the problem. Therefore, this paper hopes to combine image recognition and deep learning techniques to assist wound record personnel in reducing the time for patients to expose wounds when recording wound statuses, and to provide some useful information on wound to assist personnel analysis.
The purpose of this paper is to design a wound information identification system(WIIS). For open and connected chronic wounds, after the wound is photographed together with the provided color patches, the wound information can be identified through the wound image, and the wound information can be provided to assist the recording. For example, the wound area, the proportion distribution of the wound color, etc., to achieve the auxiliary wound record, reducing the burden on the person.
This paper presents a Wound contour positioning based on Deep Learning, WcpCNN (Wound contour positioning based on CNN) and WcpMLP (Wound contour positioning based on MLP). Using WcpMLP to detect wound contour, compared with the traditional Canny, can achieve the lowest wound area error. And according to the experiment, when the camera lens is parallel to the wound, the rotation will not cause an error. However, when the camera angle is tilted, the larger the angle, the greater the error caused by the rotation.

誌謝 I
摘 要 II
ABSTRACT III
目 錄 IV
圖目錄 VI
表目錄 VIII
第1章 前言 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 論文綱要 2
第2章 背景知識 5
2-1 傷口簡介 5
2-1-1 壓瘡傷口 5
2-1-2 壓瘡定性評估 5
2-1-3 壓瘡定量評估 6
2-1-4 傷口測量方法介紹 10
2-2 人工智慧(ARTIFICIAL INTELLIGENCE) 10
2-2-1 機器學習 11
2-2-2 深度學習 12
2-3 影像辨識技術 14
2-3-1 色彩空間 14
2-3-2 影像前處理 14
2-3-3 邊緣檢測 15
2-3-4 輪廓檢測 16
2-4 影像辨識在醫學影像上的應用 16
2-5 影像處理在傷口影像上的應用 18
第3章 傷口資訊辨識系統WIIS 21
3-1 WIIS設計 21
3-2 手動定位傷口位置 22
3-3 傷口資訊辨識流程 23
3-4 傷口輪廓檢測模型 24
3-4-1 基於Canny的傷口輪廓檢測模型 24
3-4-2 基於深度學習的傷口輪廓檢測模型 29
3-5 傷口實際面積辨識 33
3-6 傷口長寬測量 35
3-7 傷口顏色統計分析 39
第4章 傷口辨識效能分析 43
4-1 實驗環境 43
4-2 傷口輪廓檢測效能分析 43
4-3 拍照角度效能分析 48
4-4 WIIS在BWAT上的輔助 50
第5章 結論與未來工作 53
5-1 結論 53
5-2 未來工作 53
第6章 參考文獻 55

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