paper-with-me

홈 › Papers

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

2024-08-31 · Seyedeh Fahimeh Hosseini, Faezeh Shalbafzadeh, Behzad Amanpour-Gharaei

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.

📄 PDF Abstract BibTeX arXiv:2409.00442

Code (1)

Behzad-Amanpour/medical_image_pre-processing/tree/main/Body_Background_Separation 공식 구현

Tasks

Computed Tomography (CT)

Similar Papers 제목 키워드 기반

BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients

2020-06-01 · Maria de la Iglesia Vayá, Jose Manuel Saborit, Joaquim Angel Montell, Antonio Pertusa 외

This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with t…

Computed Tomography (CT)DiagnosticSemantic Segmentation

SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images

2020-12-04 · Ke Yan, Jinzheng Cai, Dakai Jin, Shun Miao 외

Radiological images such as computed tomography (CT) and X-rays render anatomy with intrinsic structures. Being able to reliably locate the same anatomical structure across varying images is a fundamental task in medical…

AnatomyComputed Tomography (CT)Contrastive LearningImage Registration+3

OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis

2026-02-04 · Luca Zedda, Andrea Loddo, Cecilia Di Ruberto arxiv

Radiological analysis increasingly benefits from pretrained visual representations that can support heterogeneous downstream tasks across imaging modalities. In this work, we introduce OmniRad, a self-supervised radiolog…

Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model

2021-08-20 · Kevin Lybarger, Aashka Damani, Martin Gunn, Ozlem Uzuner 외

Medical imaging is critical to the diagnosis and treatment of numerous medical problems, including many forms of cancer. Medical imaging reports distill the findings and observations of radiologists, creating an unstruct…

DiversityRelationRelation Extraction

Robust Dual-Graph Regularized Moving Object Detection

2022-04-25 · Jing Qin, Ruilong Shen, Ruihan Zhu, Biyun Xie

Moving object detection and its associated background-foreground separation have been widely used in a lot of applications, including computer vision, transportation and surveillance. Due to the presence of the static ba…

Moving Object DetectionObjectobject-detectionObject Detection