A new Probabilistic Active Contour region-based method for multiclass medical image segmentation

Arce-Santana, Edgar R.; Mejía-Rodríguez, Aldo R.; Martinez-Peña, Enrique; Alba, Alfonso; Méndez, Martín O.; Scalco, Elisa; Mastropietro, Alfonso; Rizzo, Giovanna

doi:10.1007/s11517-018-1896-y

Cited by 15 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e derivative of the function f (x, y) could obtain the maximum value at a point; then, its direction was represented with equation (6), its modulus value could be calculated with equation (7), and the modulus determination function gradient was indicated by equation (8); then, the operator of a gradient modulus could be defined as equation (9). In the following equations, z represents the partial derivative, ∇ represents the gradient, and G refers to the gradient operator: arc tan zf/zy zy/zx , (…”

Section: Ct Image Based On Edge Correction Algorithmmentioning

confidence: 99%

“…e current image segmentation algorithms are mainly divided into regionbased image segmentation, edge-based image segmentation, and specific theory-based image segmentation [7]. Edge-based image segmentation cannot effectively segment the image because the calculation of its derivative is extremely sensitive to noise, so it is necessary to reduce the impact of noise during image preprocessing [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computed Tomography Image Segmentation Using Edge Correction Algorithm for Refractory Mycoplasma Pneumonia in Children

Wu¹,

Ji²,

Zhao

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

Objective. It is to study the application of edge correction algorithm (ECA) in computed tomography (CT) medical image segmentation, explore its guiding significance in the analysis of clinical characteristics of children with refractory mycoplasma pneumoniae (RMPP), and discuss the therapeutic value of fiberoptic bronchoscopy bronchoalveolar lavage (BAL) for RMPP. Methods. The accuracy of ECA in CT medical image segmentation of children with RMPP was compared with that of the watershed segmentation algorithm (WSA) and swarm intelligence optimization algorithm (SIOA). The clinical characteristics and the imaging characteristics of 80 children with RMPP admitted to hospital from January 2018 to January 2020 were retrospectively analyzed based on the ECA. All children were divided into a lavage group (BAL group, n = 69) and a nonlavage group (non-BAL group, n = 11) according to whether fiberoptic bronchoscopy and BAL were performed. Bronchoscopy was adopted to analyze the cytological characteristics of BAL fluid (BALF) in children, and the recovery rate and the total effective rate of the two groups of children were observed and compared. Results. The overall accuracies (OAs) of the three ECAs (Roberts operator (RO), Sobel operator (SO), and Prewitt operator (PO)) were higher than that of WSA and SIOA, their false negative rate (FNR) and false positive rate (FPR) were small, and their denoising performance was superior to that of WSA and SIOA. The main clinical manifestations of all children were high fever, irritating dry cough, and few early signs. The results of chest CT examination were mainly manifested as patchy or large-scale consolidation, two lung mesh or small nodular shadows, and atelectasis. 69 cases with fiberoptic bronchoscopy showed swelling and congestion of the bronchial mucosa at the lesion site with visible viscous secretions, which was consistent with the imaging changes. The total number of cells in the BALF of children increased ( P < 0.05 ), which mainly represented the increase of neutrophils ( P < 0.05 ). The recovery rate of children with lavage (81.16%) was higher dramatically than that of the nonlavage group (45.45%). Conclusion. The ECA had good accuracy and denoising performance in lung CT image segmentation. The clinical characteristics, imaging characteristics, and cytological components of children had changed when they suffered from the RMPP, and fiberoptic bronchoscopy lavage had a therapeutic effect on it.

show abstract

Section: Ct Image Based On Edge Correction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computed Tomography Image Segmentation Using Edge Correction Algorithm for Refractory Mycoplasma Pneumonia in Children

Wu¹,

Ji²,

Zhao

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…The used BrainWeb data set consists of artificially simulated MRI data and was originally designed to validate various segmentation algorithms as a known basic truth [20]. It exhibits similarity of image morphological structures to in vivo acquired MRI data [20,38] and is used frequently in studies for verification purposes [1,16,19].…”

Section: Limitations Of the Studymentioning

confidence: 99%

Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus—transfer learning from existing algorithms

et al. 2020

View full text Add to dashboard Cite

Background For the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a classical machine learning algorithm. It demonstrated good clinical usability and a very accurate correlation of the volumes to the single area determination in a reproducible axial layer. This study aims to investigate whether these established segmentation algorithms can be transferred to new, more generalizable deep learning algorithms employing an extended transfer learning procedure and whether medically meaningful segmentation is possible. Methods Ninety-five routinely performed true FISP MRI sequences were retrospectively analyzed in 43 patients with pediatric hydrocephalus. Using a freely available and clinically established segmentation algorithm based on a hidden Markov random field model, four classes of segmentation (brain, cerebrospinal fluid (CSF), background, and tissue) were generated. Fifty-nine randomly selected data sets (10,432 slices) were used as a training data set. Images were augmented for contrast, brightness, and random left/right and X/Y translation. A convolutional neural network (CNN) for semantic image segmentation composed of an encoder and corresponding decoder subnetwork was set up. The network was pre-initialized with layers and weights from a pre-trained VGG 16 model. Following the network was trained with the labeled image data set. A validation data set of 18 scans (3289 slices) was used to monitor the performance as the deep CNN trained. The classification results were tested on 18 randomly allocated labeled data sets (3319 slices) and on a T2-weighted BrainWeb data set with known ground truth. Results The segmentation of clinical test data provided reliable results (global accuracy 0.90, Dice coefficient 0.86), while the CNN segmentation of data from the BrainWeb data set showed comparable results (global accuracy 0.89, Dice coefficient 0.84). The segmentation of the BrainWeb data set with the classical FAST algorithm produced consistent findings (global accuracy 0.90, Dice coefficient 0.87). Likewise, the area development of brain and CSF in the long-term clinical course of three patients was presented. Conclusion Using the presented methods, we showed that conventional segmentation algorithms can be transferred to new advances in deep learning with comparable accuracy, generating a large number of training data sets with relatively little effort. A clinically meaningful segmentation possibility was demonstrated.

show abstract

“…To deal with the heterogeneities in the regions of interest, a probabilistic active contour region‐based multi‐class segmentation method (PACO), 37 which is a variant of Chan‐Vese model, was proposed. The probability density function (PDF) was used to model the heterogeneity in each segmented region.…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour

Jin

Wang

et al. 2022

Medical Physics

View full text Add to dashboard Cite

Purpose:Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learning methods. Methods:The proposed method first constructs a slice-indexed-histogram to localize the volume of interest (VOI) and estimate the probability that a voxel belongs to the liver according its intensity. The probabilistic image would be used to initialize the 3D PAC model. Secondly, a new contour indicator function, which is a component of the model, is produced by combining the gradientbased edge detection and Hessian-matrix-based surface detection. Then, a fast numerical scheme derived for the 3D PAC model is performed to evolve the initial probabilistic image into the global minimizer of the model, which is a smoothed probabilistic image showing a distinctly highlighted liver. Next, a simple regiongrowing strategy is applied to extract the whole liver mask from the smoothed probabilistic image. Finally, a B-spline surface is constructed to fit the patch of the rib cage to prevent possible leakage into adjacent intercostal tissues. Results:The proposed method is evaluated on two public datasets.The average Dice score, volume overlap error, volume difference, symmetric surface distance and volume processing time are 0.96, 7.35%, 0.02%, 1.17 mm and 19.8 s for the Sliver07 dataset, and 0.95, 8.89%, −0.02%, 1.45 mm and 23.08 s for the 3Dircadb dataset, respectively. Conclusions:The proposed fully-automatic approach can effectively segment the liver from low-contrast and complex backgrounds. The quantitative and qualitative results demonstrate that the proposed segmentation method outperforms state-of -the-art traditional automatic liver segmentation algorithms and achieves very competitive performance compared with recent deep leaning-based methods.

show abstract

A new Probabilistic Active Contour region-based method for multiclass medical image segmentation

Cited by 15 publications

References 26 publications

Computed Tomography Image Segmentation Using Edge Correction Algorithm for Refractory Mycoplasma Pneumonia in Children

Computed Tomography Image Segmentation Using Edge Correction Algorithm for Refractory Mycoplasma Pneumonia in Children

Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus—transfer learning from existing algorithms

Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour

Contact Info

Product

Resources

About