Fast image segmentation for pulmonary lesions using hybrid level set model

The lung cancer radiotherapy treatment widely depends on adequate diagnosis. The radiologists intend to reach an image segmentation efficiency in terms of accuracy and low computation cost. However, the pulmonary lesions segmentation is still considered as a challenging task due to the noise and intensity inhomogeneity present in Computed Tomography (CT). In this study, we proposed to accelerate the nonlinear adaptive level set model, using the Bayesian rule, by incorporated the double well potential in the regularization term to get accurate and fast pulmonary lesion segmentation in CT images. We have tested the proposed method on different sized and localized lesions. All the images were taken from the database without any preprocessing. The experimental results show significant speed improvement without losing the precision of segmentation.

• Publication year

  2019

• Venue

  IEEE International Conference on Services Computing

• Publication date

  2019-12-01

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1109/SCC47175.2019.9116144
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network

  2018cited by this paper
• Auditing local methods for quality assurance in radiotherapy using the same set of predefined treatment plans

  2018cited by this paper
• Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning

  2018cited by this paper
• 3-D segmentation of lung nodules using hybrid level sets

  2018cited by this paper
• Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy

  2018cited by this paper
• Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach

  2018cited by this paper
• Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

  2017cited by this paper
• Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis

  2017cited by this paper
• Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): A region-based level set method

  2017cited by this paper
• Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation

  2017cited by this paper
• A level-set approach to joint image segmentation and registration with application to CT lung imaging

  2017cited by this paper
• Nonparametric Statistical Active Contour Based on Inclusion Degree of Fuzzy Sets

  2016cited by this paper
• A novel dual minimization based level set method for image segmentation

  2016cited by this paper
• A novel level set model with automated initialization and controlling parameters for medical image segmentation

  2016cited by this paper
• Tumor volume fuzzification for intelligent cancer staging

  2015cited by this paper
• Automatic detection of large pulmonary solid nodules in thoracic CT images.

  2015cited by this paper
• A novel approach of lung segmentation on chest CT images using graph cuts

  2015cited by this paper
• Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor

  2014cited by this paper
• A Nonlinear Adaptive Level Set for Image Segmentation

  2014influential reference
• A Curve Evolution Approach for Unsupervised Segmentation of Images With Low Depth of Field

  2013cited by this paper
• Hybrid segmentation of breast cancer cell images using a new fuzzy active contour model and an enhanced watershed method

  2013cited by this paper
• Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models

  2012cited by this paper
• Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm

  2011cited by this paper
• Distance Regularized Level Set Evolution and Its Application to Image Segmentation

  2010cited by this paper
• ctive contours driven by local and global intensity fitting energy with pplication to brain MR image segmentation

  2009cited by this paper
• Medical Image Segmentation Based on the Bayesian Level Set Method

  2007cited by this paper
• SUBMITTED TO IEEE TRANSACTIONS ON IMAGE PROCESSING Active contours without edges

  2007cited by this paper
• A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape

  2007cited by this paper
• Level set evolution without re-initialization: a new variational formulation

  2005cited by this paper
• Toward automated segmentation of the pathological lung in CT

  2005cited by this paper
• Automated lung segmentation in X-ray computed tomography: development and evaluation of a heuristic threshold-based scheme.

  2003cited by this paper
• Lung area extraction from 3D chest X-ray CT images using a shape model generated by a variable Be'zier surface

  2003cited by this paper
• Automated detection of lung nodules in CT scans: effect of image reconstruction algorithm.

  2003cited by this paper
• Lung lobe segmentation by graph search with 3D shape constraints

  2001cited by this paper
• Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images

  2001cited by this paper
• Computer recognition of regional lung disease patterns.

  1999cited by this paper
• On segmentation of lung parenchyma in quantitative computed tomography of the lung.

  1998cited by this paper
• Region-Growing Based Feature Extraction Algorithm for Tree-Like Objects

  1996cited by this paper
• Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

  1988cited by this paper
• Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. Final report

  1987cited by this paper
• Submitted to Ieee Transactions on Image Processing a Nonparametric Statistical Method for Image Segmentation Using Information Theory and Curve Evolution

  year unknowncited by this paper

• No citing papers are available for this paper.