肝臟電腦斷層影像分割透過影像處理方法

Abstract

影像分割的目標是從原始圖片中提取出感興趣的區域。過去，要分析肝臟或進行肝容量測定，都必須請胃腸肝膽科醫師或放射科醫師來判讀和分割肝臟區域，這是一項耗時且繁瑣的任務。因此，以機器取代人工處理成為首要目標。影像分割通常使用深度學習方式來解決，但醫學影像的複雜度與多樣性非常高，需要大量的影像資料和標註資料才能準確地描繪肝臟的輪廓。因此，本研究的目標是將人工分割肝臟影像區域的知識轉換為影像處理的方法，例如邊緣偵測、濾波、形態學等等。透過數據分析，以少量的臨床影像資料來達成這個目標。 本研究旨在透過利用專業醫師手動分割肝臟影像的經驗與知識，將其轉化為電腦視覺領域的影像處理方法。這些方法可能包括基於圖像梯度的邊緣檢測演算法，通過檢測像素值的變化來確定物體邊界；濾波技術，用於去除噪聲和平滑圖像；形態學處理，用於去除圖像破碎的區域以及補齊圖像的破洞。透過這些技術的結合，我們希望能夠自動地、高效地提取出肝臟的區域，減輕繁瑣的手動操作。 總而言之，本研究旨在實現一種能夠自動分割肝臟影像區域的演算法，從而減輕醫生的工作負擔，提高診斷效率。透過將醫學專業知識與電腦視覺技術相結合，我們期望能夠用少量的影像資料來達成這一目標，為醫學影像領域的自動化和智慧化做出貢獻。

The goal of image segmentation is to extract regions of interest from original images. In the past, analyzing the liver or measuring liver volume required gastroenterologists or radiologists to manually interpret and segment liver regions, which was a time-consuming and tedious task. Therefore, the primary objective is to replace manual labor with machines. Image segmentation is typically addressed using deep learning methods, but the complexity and diversity of medical images demand a large amount of annotated data to accurately depict the liver's contours. Hence, this research aims to transform the knowledge of manually segmenting liver images into image processing techniques such as edge detection, filtering, and morphology. Data analysis will be employed with a small set of clinical image data to achieve this objective. This study intends to leverage the expertise and knowledge of professional physicians in manually segmenting liver images, converting it into image processing methods in the field of computer vision. These methods may include edge detection algorithms based on image gradients to determine object boundaries by detecting pixel value variations, filtering techniques to remove noise and smoothen images, and morphological operations to remove fragmented areas and fill image holes. By combining these techniques, our aim is to automatically and efficiently extract liver regions, alleviating the burden of laborious manual tasks. In conclusion, this research seeks to develop an algorithm for automatic liver segmentation from medical images, reducing the workload of physicians and improving diagnostic efficiency. By integrating medical expertise with computer vision techniques, we hope to achieve this goal with a small amount of image data, contributing to the automation and intelligence of the field of medical imaging.