Tibia Bone Segmentation in X-ray Images - A Comparative Analysis

Jacob, Nathanael E.; Wyawahare, Medha

doi:10.5120/13277-0873

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…In this study the canny edgedetection clearly segmented the edges of the bone region in xray image. Subramoniam et al classified the arthritis in knee x-ray images using K-Nearest Neighbour (KNN) and bayesian classifiers based on feature extraction from local binary pattern [23]. They used Euclidean distance between vectors for KNN and kernel distribution for bayesian classifier to extract features.…”

Section: Discussionmentioning

confidence: 99%

Automated segmentation of knee thermal imaging and X-ray in evaluation of rheumatoid arthritis

Snekhalatha¹,

Rajalakshmi²,

Gobikrishnan³

2018

IJET

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Rheumatoid arthritis (RA) is a long lasting autoimmune disorder that affects the multiple joints of human body. The aim and objective of the study was i) to implement the automated segmentation of knee x-ray image and thermal image using fuzzy c means and canny edge detection algorithm. ii) To compare both the imaging modalities by means of feature extraction and correlate with the biochemical method as standard. Fifteen subjects with RA in knee region and 15 healthy controls were included in this study. The segmentation of thermal images was performed using fuzzy c-means algorithm and x-ray segmentation was implemented using canny edge detection algorithm. The skin surface temperature were measured in the thermal image of knee region in both RA and control subjects. The features were extracted from the segmented region of the knee x-ray image. The automated segmentation implemented in thermal imaging provided better results compared to x-ray image segmentation process. The thermal imaging feature and x-ray imaging features correlated significantly with the standard parameters.

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Section: Discussionmentioning

confidence: 99%

Automated segmentation of knee thermal imaging and X-ray in evaluation of rheumatoid arthritis

Snekhalatha¹,

Rajalakshmi²,

Gobikrishnan³

2018

IJET

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“…Both segmented images (manual and automatic) are binary, where oil is represented by value 0 and water by value 1. TABLE II presents the parameters used to compare the segmentation methods in this work [33]. The results can be seen in Figure 11.…”

Section: Segmentationmentioning

confidence: 97%

Film thickness measurement in oil–water pipe flow using image processing technique

Riaño

Rodriguez

Bannwart

et al. 2015

Experimental Thermal and Fluid Science

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“…Medical image segmentation is a necessary but a challenging problem in most image analysis and classification problems. In the process of digital radiograph acquisition, an intrinsic effect will be caused when radiation intensities exposed unevenly on the examined subject [ 12 , 13 ]. Owing to the influence of the uneven radiation intensity and various man-made factors, most hand radiographs have motion artifacts, noise, and asymmetric illumination.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

Jia

et al. 2020

International Journal of Biomedical Imaging

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In the study of pediatric automatic bone age assessment (BAA) in clinical practice, the extraction of the object area in hand radiographs is an important part, which directly affects the prediction accuracy of the BAA. But no perfect segmentation solution has been found yet. This work is to develop an automatic hand radiograph segmentation method with high precision and efficiency. We considered the hand segmentation task as a classification problem. The optimal segmentation threshold for each image was regarded as the prediction target. We utilized the normalized histogram, mean value, and variance of each image as input features to train the classification model, based on ensemble learning with multiple classifiers. 600 left-hand radiographs with the bone age ranging from 1 to 18 years old were included in the dataset. Compared with traditional segmentation methods and the state-of-the-art U-Net network, the proposed method performed better with a higher precision and less computational load, achieving an average PSNR of 52.43 dB, SSIM of 0.97, DSC of 0.97, and JSI of 0.91, which is more suitable in clinical application. Furthermore, the experimental results also verified that hand radiograph segmentation could bring an average improvement for BAA performance of at least 13%.

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Tibia Bone Segmentation in X-ray Images - A Comparative Analysis

Cited by 6 publications

References 23 publications

Automated segmentation of knee thermal imaging and X-ray in evaluation of rheumatoid arthritis

Automated segmentation of knee thermal imaging and X-ray in evaluation of rheumatoid arthritis

Film thickness measurement in oil–water pipe flow using image processing technique

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

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