利用可擴展形狀蛇模型自動分割表皮組織影像

Abstract

周邊神經負責感知外界刺激並將訊息傳遞至中樞神經系統，由中樞神經作出決策以完成身體動作。然而，當神經因病變或外來因素受損，將導致訊息傳導功能失常，進而引發身體功能異常，常見的疾病如糖尿病和家族性澱粉樣多發性神經病變，其診斷往往依賴染色神經影像。透過分割表皮組織區域，可以提取與病變相關的關鍵特徵。然而，人工標註表皮組織既耗時又費力。本研究提出了一種自動化影像分割方法，通過影像處理技術、最大連通分量和骨架化來獲取初始輪廓。此外，本研究結合了傳統主動輪廓模型與進階可變形模型，提出了一個新的分割模型，將新的外力作為曲線移動的方向。由於表皮組織影像規模龐大，為了縮短分割時間，我們將分割過程分為三個步驟。在使用134張表皮神經組織影像進行分割測試中，平均戴斯係數達到80.40%，優於其他比較的方法。本論文所提之改良主動輪廓模型能自動分割表皮組織區域，有潛力協助醫生進行相關研究。

Peripheral nerves are responsible for sensing external stimuli and transmitting the information to the central nervous system, where decisions are made to perform bodily movements. However, the signal transmission becomes impaired and leads to dysfunction in the body when nerves are damaged due to disease or external factors. Common diseases associated with such damage, such as diabetes and familial amyloid polyneuropathy, often rely on stained nerve images for accurate diagnosis. By segmenting the epidermal tissue regions, key features related to the pathology can be extracted. However, manually annotating the epidermal tissue is time-consuming. This thesis proposes an automated image segmentaion method that utilizes image processing techniques, maximum connected components and skeletonization to obtain the initial contours. Additionally, a new image segmentation system is introduced by combining traditional snakes with advanced deformable models, where a new external force is proposed to guide the contour movement. Given the large size of epidermal tissue images, the segmentation process is divided into three major steps to reduce the processing time. In the experiments using 134 epidermal nerve tissue images, the proposed method achieved an average Dice coefficient of 80.40%, outperforming other snake methods. The proposed image segmentation model based on an improved snake can automatically segment epidermal tissue regions, providing potential assistance to physicians in doing related research.