Computer-aided recognition of small rounded pneumoconiosis opacities in chest X-rays

Savol, A. M.; Li, C. C.; Hoy, R. J.

doi:10.1109/tpami.1980.6592371

Cited by 31 publications

(14 citation statements)

References 4 publications

(4 reference statements)

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“…Several computerized schemes have been developed for the detection and classification of pneumoconiosis on chest radiographs. Some studies [2,18,19] detected the small rounded or somewhat irregular opacities on a chest radiograph and then classified the radiograph as a normal radiograph or a pneumoconiosis one according to the standardized system for classifying radiographic abnormalities of pneumoconiosis as established by the International Labor Organization. Other studies [3][4][5]20] took the texture analysis on the chest radiographs to diagnose pneumoconiosis.…”

Section: Discussionmentioning

confidence: 99%

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Support Vector Machine Model for Diagnosing Pneumoconiosis Based on Wavelet Texture Features of Digital Chest Radiographs

Zhu

Chen

et al. 2013

J Digit Imaging

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This study aims to explore the classification ability of decision trees (DTs) and support vector machines (SVMs) to discriminate between the digital chest radiographs (DRs) of pneumoconiosis patients and control subjects. Twenty-eight wavelet-based energy texture features were calculated at the lung fields on DRs of 85 healthy controls and 40 patients with stage I and stage II pneumoconiosis. DTs with algorithm C5.0 and SVMs with four different kernels were trained by samples with two combinations of the texture features to classify a DR as of a healthy subject or of a patient with pneumoconiosis. All of the models were developed with fivefold cross-validation, and the final performances of each model were compared by the area under receiver operating characteristic (ROC) curve. For both SVM (with a radial basis function kernel) and DT (with algorithm C5.0), areas under ROC curves (AUCs) were 0.94 ± 0.02 and 0.86 ± 0.04 (P = 0.02) when using the full feature set and 0.95 ± 0.02 and 0.88 ± 0.04 (P = 0.05) when using the selected feature set, respectively. When built on the selected texture features, the SVM with a polynomial kernel showed a higher diagnostic performance with an AUC value of 0.97 ± 0.02 than SVMs with a linear kernel, a radial basis function kernel and a sigmoid kernel with AUC values of 0.96 ± 0.02 (P = 0.37), 0.95 ± 0.02 (P = 0.24), and 0.90 ± 0.03 (P = 0.01), respectively. The SVM model with a polynomial kernel built on the selected feature set showed the highest diagnostic performance among all tested models when using either all the wavelet texture features or the selected ones. The model has a good potential in diagnosing pneumoconiosis based on digital chest radiographs.

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Section: Discussionmentioning

confidence: 99%

“…Studies investigating CAD for pneumoconiosis date back to the 1970s, with a recent revival of interest in the late 1990s [2,3]. Yu et al [4] detected pneumoconiosis using the gray-level histogram features and the co-occurrence matrices features on digital radiographs (DRs).…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Model for Diagnosing Pneumoconiosis Based on Wavelet Texture Features of Digital Chest Radiographs

Zhu

Chen

et al. 2013

J Digit Imaging

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“…For the reason, it is suggested that the existing system [1] can not extract small round opacities from chest X-ray images obtained with CCD scanner. Similarly, since the texture analysis is significantly affected by the image quality, texture analysis [2]- [4] could not effectively extract features from the images. Therefore, it would be necessary to propose a novel method to design a CAD for pneumoconiosis in picture images obtained with CCD scanner.…”

Section: Categorization Of Pneumoconiosis X-ray Imagesmentioning

confidence: 99%

“…The current CAD systems for pneumoconiosis are broadly distinguished into two ways: the one measures the abnormalities extracting features obtained by texture analysis [1], another one extracts small round opacities from lung images and measures their size and number as well as the real diagnosis by doctors [2]- [4]. All the systems were proposed to images obtained with a special scanner such as a drum scanner or a film scanner.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner

Abe

Tahori²,

Minami³

et al. 2013

ACIS

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This paper presents a discrimination of pneumoconiosis X-ray images obtained with a common CCD scanner. Current computer-aided diagnosis systems of pneumoconiosis have been proposed to images obtained with a special scanner such as a drum scanner or a film scanner for X-ray pictures. However, since the special scanners need a large storage space and the scanners and commitment of the imaging need high-priced costs, the systems are not practical in small clinics. In this paper, we propose features for measuring abnormalities of pneumoconiosis as variables for the discrimination. Devices in the proposed system are only a tablet PC and a CCD scanner. In images obtained with CCD scanner, abnormal levels of pneumoconiosis could depend on density distribution in rib areas. Therefore, the proposed method measures the abnormalities by extracting characteristics of the distribution in the areas. Besides, using the abnormalities, the proposed method discriminates chest X-ray images into normal or abnormal cases of pneumoconiosis. Experimental results of the discriminations for 59 right-lung images have shown that the proposed abnormalities are well extracted for the discrimination.

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“…CAD systems for pneumoconiosis have been reported since 1970s [1][2][3][4]. Their measurements of abnormalities for pneumoconiosis broadly are two ways: the one measures the abnormalities based on texture analysis [1,2], another one extracts small round opacities and measures their size and number as well as the real diagnosis by diagnosticians [3,4]. All the systems were proposed to images obtained by a custom-made scanner (e.g., a drum scanner or a film scanner).…”

Section: Introductionmentioning

confidence: 99%

Application of a Computer-aid Diagnosis of Pneumoconiosis for CR X-ray Images

Abe

Minami

Miyazaki

et al. 2014

JBEMi

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Computer-aided recognition of small rounded pneumoconiosis opacities in chest X-rays

Cited by 31 publications

References 4 publications

Support Vector Machine Model for Diagnosing Pneumoconiosis Based on Wavelet Texture Features of Digital Chest Radiographs

Support Vector Machine Model for Diagnosing Pneumoconiosis Based on Wavelet Texture Features of Digital Chest Radiographs

Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner

Application of a Computer-aid Diagnosis of Pneumoconiosis for CR X-ray Images

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