Development and evaluation of a computer‐aided diagnostic scheme for lung nodule detection in chest radiographs by means of two‐stage nodule enhancement with support vector classification

Chen, Sheng; Suzuki, Kenji; MacMahon, Heber

doi:10.1118/1.3561504

Cited by 71 publications

(51 citation statements)

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“…The mean diameter of the nodules in this study was 6.4 mm, which is much smaller than the mean diameter (17 mm) of the nodules in a public database 33 that was used by many existing CAD schemes. [12][13][14][15][16][17] The contrast of the simulated nodules ranged from 5% to 15%.…”

Section: Discussionmentioning

confidence: 99%

“…The conventional CAD scheme processed each of the three images of a subject independently and discarded the correlation information between the three images, as other CAD schemes did. [12][13][14][15][16][17][18][19][20][21][22] The fusion CAD scheme included the conventional CAD scheme and two additional steps for registering all nodule candidates of a subject and integrating correlation information between the registered candidates to reduce false positives.…”

Section: Introductionmentioning

confidence: 99%

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A computerized scheme for lung nodule detection in multiprojection chest radiography

Guo

Boyce

et al. 2012

Medical Physics

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Purpose: Our previous study indicated that multiprojection chest radiography could significantly improve radiologists' performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme. Methods: Our database consisted of 59 subjects, including 43 subjects with 45 nodules and 16 subjects without nodules. The 45 nodules included 7 real and 38 simulated ones. The authors developed a conventional CAD scheme and a new fusion CAD scheme to detect lung nodules. The conventional CAD scheme consisted of four steps for (1) identification of initial nodule candidates inside lungs, (2) nodule candidate segmentation based on dynamic programming, (3) extraction of 33 features from nodule candidates, and (4) false positive reduction using a piecewise linear classifier. The conventional CAD scheme processed each of the three projection images of a subject independently and discarded the correlation information between the three images. The fusion CAD scheme included the four steps in the conventional CAD scheme and two additional steps for (5) registration of all candidates in the three images of a subject, and (6) integration of correlation information between the registered candidates in the three images. The integration step retained all candidates detected at least twice in the three images of a subject and removed those detected only once in the three images as false positives. A leave-one-subject-out testing method was used for evaluation of the performance levels of the two CAD schemes. Results: At the sensitivities of 70%, 65%, and 60%, our conventional CAD scheme reported 14.7, 11.3, and 8.6 false positives per image, respectively, whereas our fusion CAD scheme reported 3.9, 1.9, and 1.2 false positives per image, and 5.5, 2.8, and 1.7 false positives per patient, respectively. The low performance of the conventional CAD scheme may be attributed to the high noise level in chest radiography, and the small size and low contrast of most nodules. Conclusions: This study indicated that the fusion of correlation information in multiprojection chest radiography can markedly improve the performance of CAD scheme for lung nodule detection.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A computerized scheme for lung nodule detection in multiprojection chest radiography

Guo

Boyce

et al. 2012

Medical Physics

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“…Chen et al developed a CADe scheme of lung nodules in CXRs based on featurebased SVM [21]. They improved the performance by using the MTANN virtual dual-energy imaging [18].…”

Section: Cade Of Lung Nodules In Cxrmentioning

confidence: 99%

“…The ML algorithms for classification include linear discriminant analysis [34], quadratic discriminant analysis [34], multilayer perceptron [97,98], and support vector machines [145,146]. Such ML algorithms were applied to lung nodule detection in chest radiography [20,22,47,103] and thoracic CT [3,5,152,163], detection of microcalcifications in mammography [31,35,157,169], detection of masses in mammography [158], polyp detection in CT colonography [59,150,167], determining subjective similarity measure of mammographic images [82][83][84], and detection of aneurysms in brain MRI [4].…”

Section: Introductionmentioning

confidence: 99%

Computerized Detection of Lesions in Diagnostic Images

Suzuki

2015

Machine Learning in Radiation Oncology

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Computer-aided detection (CADe) has been an active research area in medical imaging. As imaging technologies advance, a large number of medical images are produced which physicians/radiologists must read. They may overlook lesions from such a large number of medical images. Consequently, CADe that provides suspicious lesions with radiologists/physicians is developed and becoming indispensable in their decision making to prevent them from overlooking lesions. Machine learning (ML) plays an essential role in CADe, because lesions and organs in medical images may be too complex to be represented accurately by a simple equation; modeling of such complex objects often requires a number of parameters that have to be determined by data. In this chapter, ML techniques used in CADe schemes for lung nodules in chest radiography and thoracic CT and those for the detection of polyps in CT colonography (CTC) are described, which include patch-/pixel-based ML and feature-based (segmented-object-based) ML.

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“…Only a few of the recently published ones are [6][7][8][9][10][11][12]. A brief description of them can be found in [13].…”

Section: Introductionmentioning

confidence: 99%

An X-ray CAD system with ribcage suppression for improved detection of lung lesions

Horváth

Orbán

Horváth

et al. 2013

Per. Pol. Elec. Eng.

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Development and evaluation of a computer‐aided diagnostic scheme for lung nodule detection in chest radiographs by means of two‐stage nodule enhancement with support vector classification

Cited by 71 publications

References 50 publications

A computerized scheme for lung nodule detection in multiprojection chest radiography

A computerized scheme for lung nodule detection in multiprojection chest radiography

Computerized Detection of Lesions in Diagnostic Images

An X-ray CAD system with ribcage suppression for improved detection of lung lesions

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