MORPHOLOGICAL SEGMENTATION OF THE KIDNEYS FROM ABDOMINAL CT IMAGES

2014 ◽  
Vol 14 (05) ◽  
pp. 1450073 ◽  
Author(s):  
AICHA BELGHERBI ◽  
ISMAHEN HADJIDJ ◽  
ABDELHAFID BESSAID
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Ct Images ◽  
Second Step ◽  
Watershed Algorithm ◽  
Morphological Filters ◽  
Abdominal Ct ◽  
Morphological Segmentation ◽  
Kidney Segmentation ◽  
Anatomical Information

The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of kidneys from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of kidneys from CT images is usually a difficult task. This difficulty is the gray's level which is similar to the spine level. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the spine by applying morphological filters. This first step makes the extraction of interest regions easier. This step is fulfilled by using various transformations such as the geodesic reconstruction. In the second step, we apply the watershed algorithm controlled by marker for kidney segmentation. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm.

scholarly journals An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6254
Author(s):  
Shaodi Yang ◽  
Yuqian Zhao ◽  
Miao Liao ◽  
Fan Zhang
Keyword(s):  
Computed Tomography ◽  
Unsupervised Learning ◽  
Medical Image ◽  
Ct Images ◽  
Abdominal Ct ◽  
Registration Method ◽  
Spatial Consistency ◽  
Net Framework ◽  
Organ Registration ◽  
Coarse To Fine

Medical image registration is an essential technique to achieve spatial consistency geometric positions of different medical images obtained from single- or multi-sensor, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US) images. In this paper, an improved unsupervised learning-based framework is proposed for multi-organ registration on 3D abdominal CT images. First, the explored coarse-to-fine recursive cascaded network (RCN) modules are embedded into a basic U-net framework to achieve more accurate multi-organ registration results from 3D abdominal CT images. Then, a topology-preserving loss is added in the total loss function to avoid a distortion of the predicted transformation field. Four public databases are selected to validate the registration performances of the proposed method. The experimental results show that the proposed method is superior to some existing traditional and deep learning-based methods and is promising to meet the real-time and high-precision clinical registration requirements of 3D abdominal CT images.

scholarly journals Comparative Image Quality Analysis of Spatial Filters for Pre-processing of CT Abdominal Images

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1449-1469
Author(s):  
Ramya Mohan ◽  
S.P. Chokkalingam ◽  
Kirupa Ganapathy ◽  
A. Rama
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Median Filter ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Ct Images ◽  
Spatial Filters ◽  
Abdominal Ct ◽  
Wiener Filters ◽  
Salt And Pepper

Aim: To determine the efficient noise reduction filter for abdominal CT images. Background: Image enrichment is the first and foremost step that has to be done in all image processing applications. It is used to enhance the quality of digital images. Digital images are liable to addition of noise from various sources such as error in instrument calibration, excess staining of images, etc., Image de-noising is an enhancement technique used to remove / reduce noise present in an image. Reducing the noise of images and preserving its edges are always critical and challenging in image processing. Materials and Method: In this paper, four different spatial filters namely Mean, Median, Gaussian and Wiener were used on 100 CT abdominal images and their performances were compared against the following four parameters: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), Normalised correlation coefficient (NCC) and Normalised Absolute Error (NAE) to determine the best denoising filter for the abdominal CT images. Result: Based on the experimental parameters, the median filter had the maximum efficiency in managing salt and pepper noise than the other three filters. Both Median and Wiener filters showed efficiency in removing the Gaussian noise. Whereas, the Wiener filter demonstrated higher efficiency in reducing both Poisson and Speckle noise. Conclusion: Based on the results of this study, we can conclude that the median filter can be used to reduce Salt and Pepper noises. Median and Wiener filters are significantly better for Gaussian Noise and the Wiener filter can be used to reduce both Poisson & Speckle noise in abdominal CT images.

Towards cascaded V-Net for automatic accurate kidney segmentation from abdominal CT images

2021 ◽  
Author(s):  
Xiongbiao Luo ◽  
Wankang Zeng ◽  
Wenkang Fan ◽  
Song Zheng ◽  
Jianhui Chen ◽  
...  
Keyword(s):  
Ct Images ◽  
Abdominal Ct ◽  
Kidney Segmentation

Use of prediction equations to determine the accuracy of whole-body fat and fat-free mass and appendicular skeletal muscle mass measurements from a single abdominal image using computed tomography in advanced cancer patients

2016 ◽  
Vol 41 (1) ◽  
pp. 70-75 ◽  
Author(s):  
Robert D. Kilgour ◽  
Katrina Cardiff ◽  
Leonard Rosenthall ◽  
Enriqueta Lucar ◽  
Barbara Trutschnigg ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Skeletal Muscle ◽  
Body Fat ◽  
Ct Images ◽  
Fat Free Mass ◽  
Whole Body ◽  
Prediction Equations ◽  
Advanced Cancer Patients ◽  
Abdominal Ct ◽  
Appendicular Skeletal Muscle

Measurements of body composition using dual-energy X-ray absorptiometry (DXA) and single abdominal images from computed tomography (CT) in advanced cancer patients (ACP) have important diagnostic and prognostic value. The question arises as to whether CT scans can serve as surrogates for DXA in terms of whole-body fat-free mass (FFM), whole-body fat mass (FM), and appendicular skeletal muscle (ASM) mass. Predictive equations to estimate body composition for ACP from CT images have been proposed (Mourtzakis et al. 2008; Appl. Physiol. Nutr. Metabol. 33(5): 997–1006); however, these equations have yet to be validated in an independent cohort of ACP. Thus, this study evaluated the accuracy of these equations in estimating FFM, FM, and ASM mass using CT images at the level of the third lumbar vertebrae and compared these values with DXA measurements. FFM, FM, and ASM mass were estimated from the prediction equations proposed by Mourtzakis and colleagues (2008) using single abdominal CT images from 43 ACP and were compared with whole-body DXA scans using Spearman correlations and Bland–Altman analyses. Despite a moderate to high correlation between the actual (DXA) and predicted (CT) values for FM (rho = 0.93; p ≤ 0.001), FFM (rho = 0.78; p ≤ 0.001), and ASM mass (rho = 0.70; p ≤ 0.001), Bland–Altman analyses revealed large range-of-agreement differences between the 2 methods (29.39 kg for FFM, 15.47 kg for FM, and 3.99 kg for ASM mass). Based on the magnitude of these differences, we concluded that prediction equations using single abdominal CT images have poor accuracy, cannot be considered as surrogates for DXA, and may have limited clinical utility.

Development of Computed Tomography Image Processing Procedure for the Diagnosis of Human Respiratory Infectious Diseases: COVID-19

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Boopathi M ◽  
◽  
Khanna D ◽  
Vennila R ◽  
Rajan R ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Quantum Noise ◽  
Respiratory Infections ◽  
Ct Images ◽  
Normal Lung ◽  
Obsessive Compulsive ◽  
Beam Hardening ◽  
Compulsive Disorder ◽  
Computed Tomography Image

Computed Tomography (CT) is a non-invasive method to give CT images of every part of the human body without superimposition of end-to-end structures. Some issues in measurements with CT are limiting too few parameters like quantum noise, beam hardening, X-ray scattering by the patient, and nonlinear partial volume effects. Image processing with Adobe Photoshop, ImageJ, and Origin software have been used to achieve good quality images for numerical analysis. Statistical functions permit to investigate the general characteristics of a human respiratory infections disease. Using Automatic Diagnosis system, differentiation in diseases can be filtered out with the help of CT images. Data can be analyzed from the CT images to distinguish between a human respiratory infections disease, a common disorder like Major Depression (MD) or Obsessive-Compulsive Disorder (OCD) and a normal lung.

Radiomics and stacking regression model for measuring bone mineral density using abdominal computed tomography

2021 ◽  
pp. 028418512110681
Author(s):  
Hong Dai ◽  
Yutao Wang ◽  
Randi Fu ◽  
Sijia Ye ◽  
Xiuchao He ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Bone Mineral Density ◽  
Regression Model ◽  
Linear Model ◽  
Bone Mineral ◽  
Lumbar Vertebrae ◽  
Ct Images ◽  
Mineral Density ◽  
Abdominal Ct ◽  
Abdominal Computed Tomography

Background Measurement of bone mineral density (BMD) is the most important method to diagnose osteoporosis. However, current BMD measurement is always performed after a fracture has occurred. Purpose To explore whether a radiomic model based on abdominal computed tomography (CT) can predict the BMD of lumbar vertebrae. Material and Methods A total of 245 patients who underwent both dual-energy X-ray absorptiometry (DXA) and abdominal CT examination (training cohort, n = 196; validation cohort, n = 49) were included in our retrospective study. In total, 1218 image features were extracted from abdominal CT images for each patient. Combined with clinical information, three steps including least absolute shrinkage and selection operator (LASSO) regression were used to select key features. A two-tier stacking regression model with multi-algorithm fusion was used for BMD prediction, which can integrate the advantages of linear model and non-linear model. The prediction results of this model were compared with those using a single regressor. The degree-of-freedom adjusted coefficient of determination (Adjusted-R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the regression performance. Results Compared with other regression methods, the two-tier stacking regression model has a higher regression performance, with Adjusted-R2, RMSE, and MAE of 0.830, 0.077, and 0.06, respectively. Pearson correlation analysis and Bland–Altman analysis showed that the BMD predicted by the model had a high correlation with the DXA results (r = 0.932, difference = −0.01 ± 0.1412 mg/cm2). Conclusion Using radiomics, the BMD of lumbar vertebrae could be predicted from abdominal CT images.

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