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

Horváth, Ágnes; Orbán, Gergely; Horváth, Ákos G.; Horväth, Gábor

doi:10.3311/ppee.2079

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…Oğul et al 8 proposed a similar hybrid self‐template model to delineate the curves of ribs using Gabor filtering, which significantly improved the performance of CAD schemes by better preserving the nodule visibility while suppressing shadows of bony structures. Horáth et al 9 introduced an approach to segment bone shadows from the chest radiographic images using a dynamic programming algorithm. These methods rely on specific algorithms to detect the contours of bony structures, indicating the locations that need suppression…”

Section: Introductionmentioning

confidence: 99%

Dilated conditional GAN for bone suppression in chest radiographs with enforced semantic features

Zhou

Shen³

2020

Medical Physics

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Purpose The purpose of this essay is to improve computer‐aided diagnosis of lung diseases by the removal of bone structures imagery such as ribs and clavicles, which may shadow a clinical view of lesions. This paper aims to develop an algorithm to suppress the imaging of bone structures within clinical x‐ray images, leaving a residual portrayal of lung tissue; such that these images can be used to better serve applications, such as lung nodule detection or pathology based on the radiological reading of chest x rays. Methods We propose a conditional Adversarial Generative Network (cGAN) (Mirza and Osindero, Conditional generative adversarial nets, 2014.) model, consisting of a generator and a discriminator, for the task of bone shadow suppression. The proposed model utilizes convolutional operations to expand the size of the receptive field of the generator without losing contextual information while downsampling the image. It is trained by enforcing both the pixel‐wise intensity similarity and the semantic‐level visual similarity between the generated x‐ray images and the ground truth, via optimizing an L‐1 loss of the pixel intensity values on the generator side and a feature matching loss on the discriminator side, respectively. Results The framework we propose is trained and tested on an open‐access chest radiograph dataset for benchmark. Results show that our model is capable of generating bone‐suppressed images of outstanding quality with a limited number of training samples (N = 272). Conclusions Our approach outperforms current state‐of‐the‐art bone suppression methods using x‐ray images. Instead of simply downsampling images at different scales, our proposed method mitigates the loss of contextual information by utilizing dilated convolutions, which gains a noticeable quality improvement for the outputs. On the other hand, our experiment shows that enforcing the semantic similarity between the generated and the ground truth images assists the adversarial training process and achieves better perceptual quality.

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Section: Introductionmentioning

confidence: 99%

Dilated conditional GAN for bone suppression in chest radiographs with enforced semantic features

Zhou

Shen³

2020

Medical Physics

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“…The approach that is advised produces a mean contour distance of 1.1 millimeters and an intersection over union distance of 0.86 millimeters when applied to 249 test photographs. a one-of-a-kind method that segments x-rays through the use of a dynamic programming strategy, so releasing them from the shadows that are cast by the rib cage and the clavicle [11]. When moving to a new place, the separated shadows will no longer be present.…”

Section: Related Workmentioning

confidence: 99%

Suppressing Chest Radiograph Ribs for Improving Lung Nodule Visibility by using Circular Window Adaptive Median Outlier (CWAMO)

Kanade¹,

Helonde²

2023

IJACSA

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Chest radiograph ribs obstruct lung nodules. To see the nodule under the chest radiograph ribs, remove or suppress them. The paper describes a circular median filter approach for finding outliers in chest radiographs. The method uses 147 Japanese Society of Radiological Technology x-ray pictures (JSRT). Pixels with intensities two standard deviations above the median are median outliers. Contrast-Limited Adaptive Histogram Equalization enhances nodule visibility (CLAHE). The method is tested on modest chest radiographs and compared to the Budapest University Bone Shadow Eliminated X-Ray Dataset methodology. The initial test uses 50 modest chest radiographs (Test 1). The proposed approach is applied after active shape modelling (ASM) lung segmentation. True positive nodules are seen on 89% of chest radiographs of various subtleties. Test-2 and Test-3 used 20 subtlety-level photos. In Test-2, the peak signal-to-noise ratio (PSNR), mean-to-standard deviation ratio (MSR), and universal image quality index (IQI) are evaluated for the full image and compared to the existing algorithm. For all three parameters, the suggested technique outperforms the algorithm. Test-3 computes nodule MSR and compares it to Budapest University's Bone Shadow Eliminated Dataset and original chest radiographs. The new algorithm improved nodule area contrast by 3.83% and 23.94% compared to the original chest radiograph. This approach improves chest radiograph nodule visualization.

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“…Suppressing ribs enhances the remaining parts of the image. Áron Horváth et.al [ 56 ] followed dynamic programming approach for separating ribs and clavicles from chest radiographic image. Segmented ribs and clavicles shadows were used for removing the same from the image using difference imaging technique.…”

Section: Nodule Detectionmentioning

confidence: 99%

Review: On Segmentation of Nodules from Posterior and Anterior Chest Radiographs

Devi

Savithri

2018

International Journal of Biomedical Imaging

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Lung cancer is one of the major types of cancer in the world. Survival rate can be increased if the disease can be identified early. Posterior and anterior chest radiography and computerized tomography scans are the most used diagnosis techniques for detecting tumor from lungs. Posterior and anterior chest radiography requires less radiation dose and is available in most of the diagnostic centers and it costs less compared to the remaining diagnosis techniques. So PA chest radiography became the most commonly used technique for lung cancer detection. Because of superimposed anatomical structures present in the image, sometimes radiologists cannot find abnormalities from the image. To help radiologists in diagnosing tumor from PA chest radiographic images range of CAD scheme has been developed for the past three decades. These computerized tools may be used by radiologists as a second opinion in detecting tumor. Literature survey on detecting tumors from chest graphs is presented in this paper.

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

Abstract: TÁMOP -4.2.2.B-10/1-2010-0009 and KMR_12-1-2012-0122.

Cited by 11 publications

References 32 publications

Dilated conditional GAN for bone suppression in chest radiographs with enforced semantic features

Dilated conditional GAN for bone suppression in chest radiographs with enforced semantic features

Suppressing Chest Radiograph Ribs for Improving Lung Nodule Visibility by using Circular Window Adaptive Median Outlier (CWAMO)

Review: On Segmentation of Nodules from Posterior and Anterior Chest Radiographs

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