A study about evolutionary and non-evolutionary segmentation techniques on hand radiographs for bone age assessment

Simu, Shreyas; Lal, Shyam

doi:10.1016/j.bspc.2016.11.016

Cited by 21 publications

(9 citation statements)

References 22 publications

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“…The initial results on a minimum dataset indicated that hand radiographs are an appropriate approach for human identification. Table-III Predicted Group Membership 1 2 3 4 5 6 7 8 9 10 Total 1 60 0 10 0 10 0 20 0 0 0 100 2 10 50 10 0 0 0 20 0 0 10 100 3 10 10 30 0 30 0 20 0 0 0 100 4 0 0 0 70 0 0 0 30 0 0 100 5 Hand radiograph segmentation for radius and ulna bones [22] The result of segmentation are above average value, i.e., 3.5 for 19 images (1 for each age group from 0 to 18 year) from Children's Hospital Los Angeles, USA…”

Section: Discussionmentioning

confidence: 99%

“…Simu et al [21] suggested an automated extraction approach for radius and ulna bones. Simu and Lal [22] compared evolutionary and non-evolutionary segmentation algorithms for hand radiographs, which identify the shapes and borders of bones. Yuh et al [23] proposed an algorithm that is used for both analyzing and extracting texture features of phalanges ROI of hand radiographs and it can be used to give firm and effective stage bone age assessment.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Analytic Technique for Forensic Human Identification based on Dual Cross Patterns of Hand Radiographs

Joshi*,

Kanphade

2020

IJITEE

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In recent times, the necessity for personal identification systems has increased due to several accidents. Under circumstances of human-made and natural disasters, it is not possible to employ a traditional biometric system. Hence, biometric radiographs of the skull, hands, and teeth are good replacement methods to identify victims. The fundamental intent of the research is to acquire a novel approach for identifying missing and anonymous individuals based on Dual Cross Pattern (DCP) features of hand radiographs. The suggested technique has contains two major steps: feature extraction and classification of the feature vectors. In this paper, an effort is made to find the most adequate classifier between the Classification Tree (CT), Feed forward Neural Network (FNN), Multiclass Support Vector Machine (m-SVM), and k-Nearest Neighbor (k-NN) based on the accuracy of retrieval of 10 adult subjects from the dataset of 300 right-hand radiographs. The classification results attained from simulation and discriminant analysis on a small primary database are encouraging

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate Analytic Technique for Forensic Human Identification based on Dual Cross Patterns of Hand Radiographs

Joshi*,

Kanphade

2020

IJITEE

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“…The X-ray image of a hand can be divided into three regions: background, soft-tissue area and bone. In the early stage of CAD BAA research, researchers tried different kinds of segmentation methods to remove the soft tissue area of the hand, and just keep the bone area for BAA [24]. Due to the gray overlap of some pixels between the bone region, soft tissue area, and background, especially in some epiphyseal regions, as shown with the red arrow in Fig.…”

Section: Basic Medical Methods Of Baa In This Studymentioning

confidence: 99%

Fine-grained precise-bone age assessment by integrating prior knowledge and recursive feature pyramid network

Jia

Zhang

et al. 2022

J Image Video Proc.

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Bone age assessment (BAA) evaluates individual skeletal maturity by comparing the characteristics of skeletal development to the standard in a specific population. The X-ray image examination for bone age is tedious and subjective, and it requires high professional skills. Therefore, AI techniques are desired to innovate and improve BAA methods. Most of the BAA method use the whole X-ray image in an end-to-end model directly. Such whole-image-based approaches fail to characterize local changes and provide limited aid for diagnosis and understanding disease progress. To address these issues, we collected and curated a dataset of 2129 cases for the study of BAA with fine-grained skeletal maturity level labels of the 13 ROIs in hand bone based on the expert knowledge from TW method. We designed a four-stage automatic BAA model based on recursive feature pyramid network. Firstly, the palm region was segmented using U-Net, followed by the extraction of multi-target ROIs of hand bone using a recursive feature pyramid network. Given the extracted ROIs, we employed a transfer learning model with attention mechanism to predict the skeletal maturity level of each ROI. Finally, the bone age is assessed based on the percentile curve of bone maturity. The proposed BAA model can automate the BAA. In addition, it provides the detection result of the 13 ROIs and their ROI-level skeletal maturity. The MAE can reach 0.61 years on the dataset with the labeling precision of one year. All the data and annotations used in this paper are released publicly.

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“…Children with short height and elevated follicle-stimulating hormone points are characterized by Carpini et al 9 In order to retrieve bone age for ultimately reaching measurements set, image processing procedures like subtraction of background, elimination of noise and recognition of soft tissue has endured numerous revisions according to Simu and Lal. 17 Maji outlines the manner in which merging and region-growing techniques are based on the similarity 18 metric and connection, 19 which might result in the epiphysis sites being fused. By using deformable representations like active shape modeling, active appearance modeling and active contour, few researchers have operated on carpal bones segmentation.…”

Section: Introductionmentioning

confidence: 99%

Detection of Turner syndrome using hand X-ray using anchor based links segmentation method

Ramachandran

Gobalakrishnan

Chokkalingam³

2022

Proc Inst Mech Eng H

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Turner Syndrome (TS) is a chromosomal disorder, wherein the female’s growth is impacted. Immature ovaries, low stature, and heart abnormalities are a range of developmental and medical issues due to TS. The condition of TS might be detected prior to birth, throughout infancy or in the early years of life. The diagnosis of TS in girls with modest symptoms and indications is sometimes deferred until they reach adolescence or become young adults. This study presents an algorithm to segment the hand digital X-ray image in children with TS. In medical image and computer vision examination, image segmentation is demanding, and very crucial. Prevailing segmentation algorithms even now suffer from common segmentation issues including under-segmentation, over-segmentation, and spurious or non-closed edges, regardless of the several years of studies. In this paper, Anchor Based Link (ABL) segmentation approach is proposed to detect TS based on fourth Metacarpal bone from left hand X-ray images. The detection of TS is demonstrated based upon the comparison of proposed approach with existing watershed segmentation and Gaussian-Mixture-Model-based Hidden-Markov-Random-Field (GMM-HMRF) method. The proposed approach attains better segmentation based on the ratio of height and width of left fourth finger that is analyzed for normal children and children having TS with the help of edge pixel present in the metacarpal bone that has been segmented. The suggested method is verified on fifty (50) sample X-ray hand images of carpal bones, providing 0.60 ± 0.02 as an average Dice coefficient.

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A study about evolutionary and non-evolutionary segmentation techniques on hand radiographs for bone age assessment

Cited by 21 publications

References 22 publications

Multivariate Analytic Technique for Forensic Human Identification based on Dual Cross Patterns of Hand Radiographs

Multivariate Analytic Technique for Forensic Human Identification based on Dual Cross Patterns of Hand Radiographs

Fine-grained precise-bone age assessment by integrating prior knowledge and recursive feature pyramid network

Detection of Turner syndrome using hand X-ray using anchor based links segmentation method

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