Computerized detection of pulmonary nodules by single-exposure dual-energy computed radiography of the chest (part 1)

Shoji, K.; Nakamura, Hajime; Ito, Wataru; Shimura, Kazuo; Kato, Hisatoyo

doi:10.1016/s0720-048x(02)00268-1

Cited by 21 publications

(15 citation statements)

References 11 publications

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“…Consequently, some researchers have investigated CAD schemes for detection of nodules on dual-energy radiographs [63], [64]. The distinction between nodules and other anatomic structures such as ribs and clavicles is improved in soft-tissue-image-like images with our technique; therefore, these images have the potential to improve the performance of nodule-detection CAD schemes.…”

Section: Discussionmentioning

confidence: 99%

Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

Suzuki

Abé

MacMahon

et al. 2006

IEEE Trans. Med. Imaging

217

157

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Abstract-When lung nodules overlap with ribs or clavicles in chest radiographs, it can be difficult for radiologists as well as computer-aided diagnostic (CAD) schemes to detect these nodules. In this paper, we developed an image-processing technique for suppressing the contrast of ribs and clavicles in chest radiographs by means of a multiresolution massive training artificial neural network (MTANN). An MTANN is a highly nonlinear filter that can be trained by use of input chest radiographs and the corresponding "teaching" images. We employed "bone" images obtained by use of a dual-energy subtraction technique as the teaching images. For effective suppression of ribs having various spatial frequencies, we developed a multiresolution MTANN consisting of multiresolution decomposition/composition techniques and three MTANNs for three different-resolution images. After training with input chest radiographs and the corresponding dual-energy bone images, the multiresolution MTANN was able to provide "bone-image-like" images which were similar to the teaching bone images. By subtracting the bone-image-like images from the corresponding chest radiographs, we were able to produce "soft-tissue-image-like" images where ribs and clavicles were substantially suppressed. We used a validation test database consisting of 118 chest radiographs with pulmonary nodules and an independent test database consisting of 136 digitized screen-film chest radiographs with 136 solitary pulmonary nodules collected from 14 medical institutions in this study. When our technique was applied to nontraining chest radiographs, ribs and clavicles in the chest radiographs were suppressed substantially, while the visibility of nodules and lung vessels was maintained. Thus, our image-processing technique for rib suppression by means of a multiresolution MTANN would be potentially useful for radiologists as well as for CAD schemes in detection of lung nodules on chest radiographs.

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Section: Discussionmentioning

confidence: 99%

Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

Suzuki

Abé

MacMahon

et al. 2006

IEEE Trans. Med. Imaging

217

157

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“…By using ES images, false-positive results caused by bony shadows were reduced in the CAD system. 25,26 By combining ES images with temporal subtraction technique, registration accuracy is improved and misregistration of artifacts on the subtraction image can be reduced. 27 The combination of these techniques and the ES technique is expected to contribute to the further improvement for diagnostic accuracy of chest radiography.…”

Section: Discussionmentioning

confidence: 99%

Detection of lung cancer using single-exposure dual-energy subtraction chest radiography

et al. 2007

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“…The technique produces soft‐tissue‐enhanced images from which bones are removed. By using these images, the performance of CAD schemes was improved . In spite of the advantages, a limited number of hospitals use DE radiography systems, because specialized equipment is required for obtaining DE x‐ray exposures, and more importantly, the radiation dose can be double in theory, comparing to that in standard CXRs.…”

Section: Introductionmentioning

confidence: 99%

Separation of bones from soft tissue in chest radiographs: Anatomy‐specific orientation‐frequency‐specific deep neural network convolution

et al. 2019

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Purpose Lung nodules that are missed by radiologists as well as by computer‐aided detection (CAD) systems mostly overlap with ribs and clavicles. Removing the bony structures would result in better visualization of undetectable lesions. Our purpose in this study was to develop a virtual dual‐energy imaging system to separate ribs and clavicles from soft tissue in chest radiographs. Methods We developed a mixture of anatomy‐specific, orientation‐frequency‐specific (ASOFS) deep neural network convolution (NNC) experts. Anatomy‐specific (AS) NNC was designed to separate the bony structures from soft tissue in different lung segments. While an AS design was proposed previously under our massive‐training artificial neural networks (MTANN) framework, in this work, we newly mathematically defined an AS experts model as well as its learning and inference strategies in a probabilistic deep‐learning framework. In addition, in combination with our AS experts design, we newly proposed the orientation‐frequency‐specific (OFS) NNC models to decompose bone and soft‐tissue structures into specific orientation‐frequency components of different scales using a multi‐resolution decomposition technique. We trained multiple NNC models, each of which is an expert for a specific orientation‐frequency component in a particular anatomic segment. Perfect reconstruction discrete wavelet transform was used for OFS decomposition/reconstruction, while we introduced a soft‐gating layer to merge the predictions of AS NNC experts. To train our model, we used the bone images obtained from a dual‐energy system as the target (or teaching) images while the standard chest radiographs were used as the input to our model. The training, validation, and test were performed in a nested two‐fold cross‐validation manner. Results We used a database of 118 chest radiographs with pulmonary nodules to evaluate our NNC scheme. In order to evaluate our scheme, we performed quantitative and qualitative evaluation of the predicted bone and soft‐tissue images from our model as well as the ones of a state‐of‐the‐art technique where the “gold‐standard” dual‐energy bone and soft‐tissue images were used as the reference images. Both quantitative and qualitative evaluations demonstrated that our ASOFS NNC was superior to the state‐of‐the‐art bone‐suppression technique. Particularly, our scheme was better able to maintain the conspicuity of nodules and lung vessels, comparing to the reference technique, while it separated ribs and clavicles from soft tissue. Comparing to a state‐of‐the‐art bone suppression technique, our bone images had substantially higher (t−test; P < 0.01) similarity, in terms of structural similarity index (SSIM) and peak signal‐to‐noise ratio (PSNR), to the “gold−standard” dual−energy bone images. Conclusions Our deep ASOFS NNC scheme can decompose chest radiographs into their bone and soft‐tissue images accurately, offering the improved conspicuity of lung nodules and vessels, and therefore would be useful for radiologists as well as CAD systems in d...

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Computerized detection of pulmonary nodules by single-exposure dual-energy computed radiography of the chest (part 1)

Cited by 21 publications

References 11 publications

Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

Detection of lung cancer using single-exposure dual-energy subtraction chest radiography

Separation of bones from soft tissue in chest radiographs: Anatomy‐specific orientation‐frequency‐specific deep neural network convolution

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