Fully automated radiogrammetric measurement of third metacarpal bone from hand radiograph

Areeckal, Anu Shaju; David, S Sumam; Kocher, Michel; Jayasheelan, Nikil; Kamath, Jagannath B

doi:10.1109/spcom.2016.7746608

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Many authors [32]- [34], [40]- [45] used a dataset of children under the age of 18 for the bone age assessment (BAA) because the segmentation of carpals and phalanges is easy as, after the age of 18, bones are fully grown, and fusion is complete. Hence, it becomes a time-consuming and challenging task to select a particular algorithm or combination of different algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…There is no public hand radiograph dataset for the comparison of the proposed human identification system, so it is difficult to make a rational comparison. The main aim of the state of art papers is to predict the bone age in the range of 0-18 years [28], [32], [40]- [45] because the bones are entirely matured, and fusion is complete after the age of 18 years. In most of the state of art papers, assessment is done successfully; however, these methods are not suitable for the identification of adult victims.…”

Section: % Cross Validation Accuracy =mentioning

confidence: 99%

“…The average classification accuracy of 89.1% was achieved. The method of [32] is tested on 157 left hand x-ray images collected from Image Processing and Informatics Lab, University of Southern California with age group 17-18 years for automatic segmentation of third metacarpal bone for diagnosis of osteoporosis. The accuracy achieved is 94.9%.…”

Section: % Cross Validation Accuracy =mentioning

confidence: 99%

“…Bone regions have been segmented using a second-order derivative filter and Hausdorff distance is used for the joint space width measurement. Areeckal et al [32] presented the method for diagnosis of osteoporosis with geometric features of the third metacarpal using the watershed segmentation technique for metacarpal segmentation. For the detection of Rheumatoid arthritis (RA) using hand radiographic images, Mihail et al [33] have offered an estimation of the hand skeletal shape using a deep CNN and conditional random fields.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Person Authentication Using Hand Radiographs: A Forensic Approach

Joshi¹,

Kanphade²

2020

IEEE Access

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Biometric radiographs have gained importance in recent times owing to the rise in crime and disaster incidents. In recent times, authentication and identification of a person has become an essential part of most of the computer vision automation systems. Conventional fingerprint, iris, face, palm prints fail to recognize the human when the external biometric parts have been damaged due to rashes, wounds, and severe burning. Security, robustness, privacy, and non-forgery are the critical aspects of any person authentication system. In such situations, identification based on radiographs of the skull, hand, and teeth are effective replacement methods. In this paper, a novel forensic hand radiograph based human authentication is proposed using a deep neural network. Three-layered convolutional deep neural network architecture is used for the feature extraction of hand radiographs and for recognition; KNN and SVM classifiers are used. As a part of the experimentation, a total of 750 hand radiographs acquired from 150 subjects of different age groups, professions, and gender are considered. The performance of the algorithm is evaluated based on crossvalidation accuracy by varying striding pixels, polling window size, kernel size, and the number of filters. Our experiment reveals that hand radiographs contain biometric information that can be used to identify humans in disaster victim identification. The experimental study also indicates that the proposed approach is significantly effective than conventional methods for the person authentication using hand radiographs. INDEX TERMS Biometrics, identification of persons, image forensics, neural networks, pattern recognition, radiography.

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