Separation of Body and Background in Radiological Images. A Practical Python Code
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912018660851712 |
|---|---|
| author | Hosseini, Seyedeh Fahimeh Shalbafzadeh, Faezeh Amanpour-Gharaei, Behzad |
| author_facet | Hosseini, Seyedeh Fahimeh Shalbafzadeh, Faezeh Amanpour-Gharaei, Behzad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00442 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Separation of Body and Background in Radiological Images. A Practical Python Code Hosseini, Seyedeh Fahimeh Shalbafzadeh, Faezeh Amanpour-Gharaei, Behzad Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Separation of Body and Background in Radiological Images. A Practical Python Code |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.00442 |