Retrospective Correction of the Heel Effect in Hand Radiographs

Behiels, Gert; Maes, Frederik; Vandermeulen, Dirk; Suetens, Paul

doi:10.1007/3-540-45468-3_36

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…In most X-ray imaging systems, large-scale background nonuniformities resulting from the heel effect can be easily corrected only using human visual perception. 5,7 However, for gain image composition, a software-based heel effect correction was essential. Without it, pixels from different image geometries would contain inconsistent intensity due to the heel effect, resulting in artifacts in the gain map.…”

Section: Discussionmentioning

confidence: 99%

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Gain Correction for an X-ray Imaging System With a Movable Flat Panel Detector and Intrinsic Localization Crosshair

Park

Sharp

2015

Technol Cancer Res Treat

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Gain calibration for X-ray imaging systems with a movable flat panel detector and an intrinsic crosshair is a challenge due to the geometry-dependent heel effect and crosshair artifact. This study aims to develop a gain correction method for such systems by implementing the Multi-Acquisition Gain Image Correction technique. Flood field images containing crosshair and heel effect were acquired in 4 different flat panel detector positions at fixed exposure parameters. The crosshair region was automatically detected using common image processing algorithms and removed by a simple interpolation procedure, resulting in a crosshair-removed image. A large kernel-based correction was then used to remove the heel effect. Mask filters corresponding to each crosshair region were applied to the resultant heel effect-removed images to invalidate the pixels of the original crosshair region. Finally, a seamless gain map was composed with corresponding valid pixels from the processed images either by the sequential replacement or by the selective averaging techniques developed in this study. Quantitative evaluation was performed based on normalized noise power spectrum and detective quantum efficiency improvement factor for the flood field images corrected by the MultiAcquisition Gain Image Correction-based gain maps. For comparison purposes, a single crosshair-removed gain map was also tested. As a result, it was demonstrated that the Multi-Acquisition Gain Image Correction technique achieved better image quality than the crosshair-removed technique, showing lower normalized noise power spectrum values over most of spatial frequencies. The improvement was more obvious at the priori-crosshair region of the gain map. The mean detective quantum efficiency improvement factor was 1.09 + 0.06, 2.46 + 0.32, and 3.34 + 0.36 in the priori-crosshair region and 2.35 + 0.31, 2.33 + 0.31, and 3.09 + 0.34 in the normal region, for crosshair-removed, Multi-Acquisition Gain Image Correction-sequential replacement, and Multi-Acquisition Gain Image Correction-selective averaging techniques, respectively. Therefore, this study indicates that the introduced Multi-Acquisition Gain Image Correction technique is an appropriate method for gain calibration of an imaging system associated with a moving flat panel detector and an intrinsic crosshair.

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Section: Discussionmentioning

confidence: 99%

“…4 Several correction methods for heel effect have been proposed based either on hardware approaches 5,6 or on computational methods. 4,7 Intrinsic radio-opaque obstacles can also cause field nonuniformity by blocking some portion of the flood field image intensities. A good example of those obstacles is an antiscatter grid.…”

Section: Introductionmentioning

confidence: 99%

Gain Correction for an X-ray Imaging System With a Movable Flat Panel Detector and Intrinsic Localization Crosshair

Park

Sharp

2015

Technol Cancer Res Treat

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“…Figure 2 presents the sequence of steps for segmenting the PROI. These steps are chosen considering that there are five types of objects in a carpal radiography image (Behiels, Maes, Vandermeulen, & Suetens, 2002): bones, tissues, background, Heel Effect and the individual identification tag. First, a contrast enhancement step is performed to correct the Heel effect, an irregular distribution of X-rays over the radiography (Olivete, Rodrigues, & do Nascimento, 2005).…”

Section: Proi Segmentationmentioning

confidence: 99%

Bone age estimation from carpal radiography images using deep learning

et al. 2020

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Bone age estimation has been used in medicine to verify whether the bone structure development degree of a person corresponds to their chronological age. Such estimate is useful for prognosis about the development of children and adolescents, as well as for the diagnosis of endocrinological diseases. This work proposes a fully automated methodology for bone age estimation from carpal radiography images. The methodology comprises two steps, the preprocessing of the image and the classification using a convolutional neural network. The system accuracy for different types of preprocessing is evaluated. We compare the accuracy achieved using the full radiography image as input for the neural network and using only parts of the image corresponding to the Phalangeal region, the Epiphyseal region, and the concatenation of these parts with a crop around the wrist. Digital image processing techniques are employed to segment these regions. Experiments are performed using radiography images from the California University Database. The impact of using different pretrained neural networks for transfer learning is evaluated.

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“…Porém, esse procedimento é comprometido pela distribuição irregular de intensidade dos raios-X, o qual ocasiona uma iluminação não uniforme no fundo das imagens. Este efeito é conhecido por efeito Heel (BEHIELS et al, 2002).…”

Section: Considerações Iniciaisunclassified

Estimativa da idade óssea através da análise carpal baseada na simplificação do método de Eklof & Ringertz

Júnior¹

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Este trabalho apresenta uma metodologia semi-automática e simplificada para estimação da idade óssea baseada no método de Eklof & Ringertz.Fundamenta-se no processamento e extração de informações de imagens radiográficas da mão, mostrando a real influência do efeito Heel nesse tipo de imagem. Apresenta resultados obtidos com a aplicação de algoritmos de thresholding, com e sem a correção do efeito Heel,e propõe uma metodologia para isolar os ossos do tecido da mão para obtenção de dimensões dos mesmos.Essas dimensões foram usadas como informações para a estimação da idade óssea de seres humanos em fase de crescimento, buscando uma simplificação do método de Eklof & Ringertz.

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Retrospective Correction of the Heel Effect in Hand Radiographs

Cited by 7 publications

References 9 publications

Gain Correction for an X-ray Imaging System With a Movable Flat Panel Detector and Intrinsic Localization Crosshair

Gain Correction for an X-ray Imaging System With a Movable Flat Panel Detector and Intrinsic Localization Crosshair

Bone age estimation from carpal radiography images using deep learning

Estimativa da idade óssea através da análise carpal baseada na simplificação do método de Eklof & Ringertz

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