Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule

Shiraishi, Junji; Katsuragawa, Shigehiko; Ikezoe, Junpei; Matsumoto, Tsuneo; Kobayashi, Takeshi; Komatsu, Ken Ichi; Matsui, Mitate; Fujita, Hiroshi; Kodera, Yoshie; Doi, Kunio

doi:10.2214/ajr.174.1.1740071

Cited by 778 publications

(182 citation statements)

References 8 publications

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“…11 We arranged them into 1000 signal-free patches, and 1000 patches with a signal inserted in the middle. The images are then artificially degraded using a Gaussian blur with parameter σ b , and subsequently corrupted with noise with variance σ 2 n .…”

Section: Resultsmentioning

confidence: 99%

Objectively measuring signal detectability, contrast, blur and noise in medical images using channelized joint observers

Goossens

Luong

Platiša

et al. 2013

Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment

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To improve imaging systems and image processing techniques, objective image quality assessment is essential. Model observers adopting a task-based quality assessment strategy by estimating signal detectability measures, have shown to be quite successful to this end. At the same time, costly and time-consuming human observer experiments can be avoided. However, optimizing images in terms of signal detectability alone, still allows a lot of freedom in terms of the imaging parameters. More specifically, fixing the signal detectability defines a manifold in the imaging parameter space on which different "possible" solutions reside. In this article, we present measures that can be used to distinguish these possible solutions from each other, in terms of image quality factors such as signal blur, noise and signal contrast. Our approach is based on an extended channelized joint observer (CJO) that simultaneously estimates the signal amplitude, scale and detectability. As an application, we use this technique to design k-space trajectories for MRI acquisition. Our technique allows to compare the different spiral trajectories in terms of blur, noise and contrast, even when the signal detectability is estimated to be equal.

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Section: Resultsmentioning

confidence: 99%

Objectively measuring signal detectability, contrast, blur and noise in medical images using channelized joint observers

Goossens

Luong

Platiša

et al. 2013

Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment

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show abstract

“…Therefore, we extract 2000 patches of size 100 × 100 from the chest radiography database of [17], and 1000 signal-free patches, and 1000 patches with a signal inserted in the middle. The images are then artificially degraded using a Gaussian blur with parameter σ b , and next corrupted with noise with variance σ 2 n .…”

Section: Resultsmentioning

confidence: 99%

Optimizing image quality using test signals: Trading off blur, noise and contrast

Goossens

Luong

Platiša

et al. 2012

2012 Fourth International Workshop on Quality of Multimedia Experience

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Objective image quality assessment (QA) is crucial in order to improve imaging systems and image processing techniques. In medical imaging, model observers that estimate signal detectability, have become widespread and promising as a means to avoid costly human observer experiments. However, signal detectability alone does not give the complete picture: one may also be interested in optimizing several independent quality factors (e.g. contrast, spatial resolution, noise).In recent work, we have proposed the channelized joint observer (CJO), to jointly detect and estimate random parametric signals in images, a so-called signal-known-statistically (SKS) detection task. In this paper, we show how the estimation capabilities of the CJO can be exploited to estimate several image quality factors in degraded images, through signal insertion. By fixing the signal detectability, we illustrate how to benefit from the trade-offs that exist between the different quality factors. Our method is in the first place intended to aid medical image reconstruction techniques and medical display design, although the technique can also be useful in a much wider context.

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“…Bobadilla et al tried a family of CNNs known as DeepCNets composed of alternating convolutional and maxpooling layers with a linearly increasing number of filters. They trained and tested the Japanese Society of Radiological Technology (JSRT) database [17] composed of 154 nodules and 93 non-nodule chest radiographs. Their results show that CNNs can operate effectively on lung nodule classification through data augmentation and dropout regularization and achieve comparable results with state-of-the-art performance [18].…”

Section: Cancer Screening In Chest X-raymentioning

confidence: 99%

Deep Learning for Cancer Screening in Medical Imaging

Jeong

2017

Hanyang Med Rev

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Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule

Abstract: This database can be useful for many purposes, including research, education, quality assurance, and other demonstrations.

Cited by 778 publications

References 8 publications

Objectively measuring signal detectability, contrast, blur and noise in medical images using channelized joint observers

Objectively measuring signal detectability, contrast, blur and noise in medical images using channelized joint observers

Optimizing image quality using test signals: Trading off blur, noise and contrast

Deep Learning for Cancer Screening in Medical Imaging

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