Measurement of smaller colon polyp in CT colonography images using morphological image processing

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2 Citations (Scopus)

Abstract

Purpose: Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques. Methods: A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge. Results: The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6–9 mm range, polyps of even <5 mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes ∼ 4 min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, TPR=87.5%,TNR=82%,PPV=94.45%,NPV=64.28%,F1score=90.66% and acuracy= 86.27 % were achieved. Conclusions: The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at α= 5 %.

Original languageEnglish
Pages (from-to)1845-1855
Number of pages11
JournalInternational journal of computer assisted radiology and surgery
Volume12
Issue number11
DOIs
Publication statusPublished - 01-11-2017

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Computed Tomographic Colonography
Polyps
Tomography
Colon
Image processing
Decision making
Processing
Decision Making
Retrospective Studies
Sensitivity and Specificity

All Science Journal Classification (ASJC) codes

  • Surgery
  • Radiology Nuclear Medicine and imaging
  • Health Informatics

Cite this

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title = "Measurement of smaller colon polyp in CT colonography images using morphological image processing",
abstract = "Purpose: Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques. Methods: A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge. Results: The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6–9 mm range, polyps of even <5 mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes ∼ 4 min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, TPR=87.5{\%},TNR=82{\%},PPV=94.45{\%},NPV=64.28{\%},F1score=90.66{\%} and acuracy= 86.27 {\%} were achieved. Conclusions: The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at α= 5 {\%}.",
author = "Manjunath, {K. N.} and Siddalingaswamy, {P. C.} and Prabhu, {G. K.}",
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N2 - Purpose: Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques. Methods: A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge. Results: The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6–9 mm range, polyps of even <5 mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes ∼ 4 min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, TPR=87.5%,TNR=82%,PPV=94.45%,NPV=64.28%,F1score=90.66% and acuracy= 86.27 % were achieved. Conclusions: The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at α= 5 %.

AB - Purpose: Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques. Methods: A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge. Results: The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6–9 mm range, polyps of even <5 mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes ∼ 4 min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, TPR=87.5%,TNR=82%,PPV=94.45%,NPV=64.28%,F1score=90.66% and acuracy= 86.27 % were achieved. Conclusions: The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at α= 5 %.

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