Hybrid Automatic Lung Segmentation on Chest CT Scans
نویسندگان
چکیده
منابع مشابه
Automatic identification of lung abnormalities in chest spiral CT scans
This research aims at developing a fully automatic ComputerAssisted Diagnosis (CAD) system for lung cancer screening using chest spiral CT scans. One thousand subjects are enrolled in a chest cancer screening program in Louisville, KY, USA, which aims at quantification of the effectiveness of low dose spiral CT scans for early diagnosis of lung cancer, and evaluating its possible impact on impr...
متن کاملAutomatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans
Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anato...
متن کاملClassification of Lung Regions using Morphometrics for Chest CT Scans
Medical diagnosis is extremely important but complicated task that should be performed accurately and efficiently. Disease diagnosis is one of the applications where data mining tools are proving successful results. Chronic obstructive pulmonary disease (also known as COPD) is a condition that makes breathing difficult. Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of...
متن کاملAutomatic Segmentation for Pulmonary Vessels in Plain Thoracic CT Scans
As pulmonary CT has a lot of noise produced by thoracic imaging and partial volume effect, it is difficult that the computer segments out the correct blood vessels for plain thoracic CT. Therefore, after a deep investigation into the enhancement, segmentation methods and the upgrading ability of fractional differential operation, the paper proposes an automatic segmentation method for pulmonary...
متن کاملAutomatic Lung Segmentation of Volumetric Low-Dose CT Scans Using Graph Cuts
We propose a new technique for unsupervised segmentation of the lung region from low dose computed tomography (LDCT) images. We follow the most conventional approaches such that initial images and desired maps of regions are described by a joint Markov-Gibbs random field (MGRF) model of independent image signals and interdependent region labels. But our focus is on more accurate model identific...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2987925