Literature DB >> 18777913

Wavelet method for CT colonography computer-aided polyp detection.

Jiang Li1, Robert Van Uitert, Jianhua Yao, Nicholas Petrick, Marek Franaszek, Adam Huang, Ronald M Summers.   

Abstract

Computed tomographic colonography (CTC) computer aided detection (CAD) is a new method to detect colon polyps. Colonic polyps are abnormal growths that may become cancerous. Detection and removal of colonic polyps, particularly larger ones, has been shown to reduce the incidence of colorectal cancer. While high sensitivities and low false positive rates are consistently achieved for the detection of polyps sized 1 cm or larger, lower sensitivities and higher false positive rates occur when the goal of CAD is to identify "medium"-sized polyps, 6-9 mm in diameter. Such medium-sized polyps may be important for clinical patient management. We have developed a wavelet-based postprocessor to reduce false positives for this polyp size range. We applied the wavelet-based postprocessor to CTC CAD findings from 44 patients in whom 45 polyps with sizes of 6-9 mm were found at segmentally unblinded optical colonoscopy and visible on retrospective review of the CT colonography images. Prior to the application of the wavelet-based postprocessor, the CTC CAD system detected 33 of the polyps (sensitivity 73.33%) with 12.4 false positives per patient, a sensitivity comparable to that of expert radiologists. Fourfold cross validation with 5000 bootstraps showed that the wavelet-based postprocessor could reduce the false positives by 56.61% (p <0.001), to 5.38 per patient (95% confidence interval [4.41, 6.34]), without significant sensitivity degradation (32/45, 71.11%, 95% confidence interval [66.39%, 75.74%], p=0.1713). We conclude that this wavelet-based postprocessor can substantially reduce the false positive rate of our CTC CAD for this important polyp size range.

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Year:  2008        PMID: 18777913      PMCID: PMC2562642          DOI: 10.1118/1.2938517

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  34 in total

1.  Automated polyp detection at CT colonography: feasibility assessment in a human population.

Authors:  R M Summers; C D Johnson; L M Pusanik; J D Malley; A M Youssef; J E Reed
Journal:  Radiology       Date:  2001-04       Impact factor: 11.105

2.  Visual image analysis by square wavelets: empirical evidence supporting a theoretical agreement between wavelet analysis and receptive field organization of visual cortical neurons.

Authors:  Willard L Brigner
Journal:  Percept Mot Skills       Date:  2003-10

3.  Automated knowledge-guided segmentation of colonic walls for computerized detection of polyps in CT colonography.

Authors:  Janne Näppi; Abraham H Dachman; Peter MacEneaney; Hiroyuki Yoshida
Journal:  J Comput Assist Tomogr       Date:  2002 Jul-Aug       Impact factor: 1.826

4.  Polyps: linear and volumetric measurement at CT colonography.

Authors:  Srinath C Yeshwant; Ronald M Summers; Jianhua Yao; Daniel S Brickman; J Richard Choi; Perry J Pickhardt
Journal:  Radiology       Date:  2006-12       Impact factor: 11.105

5.  A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications.

Authors:  Liyang Wei; Yongyi Yang; Robert M Nishikawa; Yulei Jiang
Journal:  IEEE Trans Med Imaging       Date:  2005-03       Impact factor: 10.048

6.  Support vector machines committee classification method for computer-aided polyp detection in CT colonography.

Authors:  Anna K Jerebko; James D Malley; Marek Franaszek; Ronald M Summers
Journal:  Acad Radiol       Date:  2005-04       Impact factor: 3.173

7.  Rotation-invariant multiresolution texture analysis using radon and wavelet transforms.

Authors:  Kourosh Jafari-Khouzani; Hamid Soltanian-Zadeh
Journal:  IEEE Trans Image Process       Date:  2005-06       Impact factor: 10.856

8.  Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population.

Authors:  Ronald M Summers; Jianhua Yao; Perry J Pickhardt; Marek Franaszek; Ingmar Bitter; Daniel Brickman; Vamsi Krishna; J Richard Choi
Journal:  Gastroenterology       Date:  2005-12       Impact factor: 22.682

9.  Automated polyp detector for CT colonography: feasibility study.

Authors:  R M Summers; C F Beaulieu; L M Pusanik; J D Malley; R B Jeffrey; D I Glazer; S Napel
Journal:  Radiology       Date:  2000-07       Impact factor: 11.105

10.  A computer-aided diagnosis for locating abnormalities in bone scintigraphy by a fuzzy system with a three-step minimization approach.

Authors:  Tang-Kai Yin; Nan-Tsing Chiu
Journal:  IEEE Trans Med Imaging       Date:  2004-05       Impact factor: 10.048

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  12 in total

1.  Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.

Authors:  Kenji Suzuki
Journal:  IEICE Trans Inf Syst       Date:  2013-04-01

2.  CT colonography: advanced computer-aided detection scheme utilizing MTANNs for detection of "missed" polyps in a multicenter clinical trial.

Authors:  Kenji Suzuki; Don C Rockey; Abraham H Dachman
Journal:  Med Phys       Date:  2010-01       Impact factor: 4.071

3.  Increasing computer-aided detection specificity by projection features for CT colonography.

Authors:  Hongbin Zhu; Zhengrong Liang; Perry J Pickhardt; Matthew A Barish; Jiangsheng You; Yi Fan; Hongbing Lu; Erica J Posniak; Robert J Richards; Harris L Cohen
Journal:  Med Phys       Date:  2010-04       Impact factor: 4.071

4.  A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans.

Authors:  Gökalp Tulum; Bülent Bolat; Onur Osman
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-01-18       Impact factor: 2.924

5.  Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning.

Authors:  Shijun Wang; Jianhua Yao; Nicholas Petrick; Ronald M Summers
Journal:  Int J Comput Intell Appl       Date:  2010-01-01

6.  Optimizing computer-aided colonic polyp detection for CT colonography by evolving the Pareto fronta.

Authors:  Jiang Li; Adam Huang; Jack Yao; Jiamin Liu; Robert L Van Uitert; Nicholas Petrick; Ronald M Summers
Journal:  Med Phys       Date:  2009-01       Impact factor: 4.071

7.  A review of computer-aided diagnosis in thoracic and colonic imaging.

Authors:  Kenji Suzuki
Journal:  Quant Imaging Med Surg       Date:  2012-09

8.  EMPLOYING TOPOGRAPHICAL HEIGHT MAP IN COLONIC POLYP MEASUREMENT AND FALSE POSITIVE REDUCTION.

Authors:  Jianhua Yao; Jiang Li; Ronald M Summers
Journal:  Pattern Recognit       Date:  2009       Impact factor: 7.740

9.  Registration of prone and supine CT colonography scans using correlation optimized warping and canonical correlation analysis.

Authors:  Shijun Wang; Jianhua Yao; Jiamin Liu; Nicholas Petrick; Robert L Van Uitert; Senthil Periaswamy; Ronald M Summers
Journal:  Med Phys       Date:  2009-12       Impact factor: 4.071

10.  An adaptive paradigm for computer-aided detection of colonic polyps.

Authors:  Huafeng Wang; Zhengrong Liang; Lihong C Li; Hao Han; Bowen Song; Perry J Pickhardt; Matthew A Barish; Chris E Lascarides
Journal:  Phys Med Biol       Date:  2015-09-08       Impact factor: 3.609

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