Separation of Body and Background in Radiological Images. A Practical Python Code
This provides a practical tool for researchers and clinicians needing automated body-background separation in radiological image analysis, though it appears incremental as it builds on existing segmentation concepts.
The authors tackled the problem of separating body parts from dark backgrounds in radiological images like MRI and CT scans, presenting a Python code that achieved practical separation across various body regions including brain, neck, and abdomen. They also introduced intensity normalization and outlier restriction methods to improve the process.
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.