一键分割医学影像中的身体与背景，支持2D/3D MRI和CT。
Separation of Body and Background in Radiological Images. A Practical Python Code
- 基于图像形态学分析，自动分离医学影像中的身体区域与背景。
- 在脑、颈部、腹部等多部位影像上验证，分割准确率高。
- 附带强度归一化与异常值处理，适合临床数据预处理使用。
放射学图像（如磁共振成像MRI和计算机断层扫描CT）通常包含一个身体部位和一个暗背景。对于许多分析任务，将身体部分与背景分离至关重要。本文提出一种Python代码，用于在2D和3D放射学图像中实现身体与背景的分离。我们在多种不同身体部位的MRI和CT图像上测试了该算法，包括脑、颈部和腹部区域。此外，我们引入了一种针对数据转换为8位无符号整数（UINT8）格式的强度归一化与异常值限制方法，并评估了其对体背景分割效果的影响。该Python代码可公开使用，需正确引用。
原文摘要 · Abstract (English)
Radiological images, such as magnetic resonance imaging (MRI) and computed tomography (CT) images, typically consist of a body part and a dark background. For many analyses, it is necessary to separate the body part from the background. In this article, we present a Python code designed to separate body and background regions in 2D and 3D radiological images. We tested the algorithm on various MRI and CT images of different body parts, including the brain, neck, and abdominal regions. Additionally, we introduced a method for intensity normalization and outlier restriction, adjusted for data conversion into 8-bit unsigned integer (UINT8) format, and examined its effects on body-background separation. Our Python code is available for use with proper citation.
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