Separation of Body and Background in Radiological Images. A Practical Python Code

TL;DR

This paper introduces a practical Python code for effectively separating body regions from backgrounds in 2D and 3D radiological images, facilitating medical image analysis tasks.

Contribution

The paper presents a novel Python implementation for body-background separation in MRI and CT images, including normalization and outlier handling techniques.

Findings

Effective separation across various MRI and CT images

Normalization improves segmentation accuracy

Code is publicly available for use and citation

Abstract

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.