Classification of hand bones for bone age assessment

Al-Taani, Ahmad T.; Ricketts, Ian W.; Cairns, A.Y.

doi:10.1109/icecs.1996.584610

Cited by 15 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Al-Tanni et al [4] proposed another way to deal with oversee portraying bones of the hand-wrist pictures into pediatric times of change utilizing point course models (PDM). The technique includes two stages: the plan stage and the solicitation stage.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Techniques for Bone Age Assessment using Image Processing

Kaur¹

2016

IJCA

View full text Add to dashboard Cite

Bone age assessment is an innovation which empowers us to determination the age bone with the help PC picture preparing and assessment of the computerized perceptions. In this review paper we have reviewed various methods for bone age assessment like active shape modeling random forest regression method, Greulich & Pyle method, Tanner and Whitehouse method and RUS method with their advantages and disadvantages. All of the above methods provide effective assistance in processing phase of the bone age assessment.

show abstract

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Techniques for Bone Age Assessment using Image Processing

Kaur¹

2016

IJCA

View full text Add to dashboard Cite

show abstract

“…Automated bone age estimation process is divided into four main stages, such as pre-processing, enhancement, segmentation and decision making. In literature [10][11][12][13][14][15][16][17] researchers proposed many alternative approaches for automating the bone age assessment but each of them lack in accuracy level at different levels. Thus this paper addresses a solution by applying a BPE (Bone Pixel Enhancement) algorithm to enhance the bone pixels from Epiphysis region of interest (EROI), such that the bone, soft and background pixels get separated and bone pixel edges are identified using HIPI (Homogeneity Image based on pixel intensity).…”

Section: Introductionmentioning

confidence: 99%

Segmentation of bone pixels from EROI Image using clustering method for bone age assessment

Rajitha

Agarwal

2016

SPIE Proceedings

View full text Add to dashboard Cite

The bone age of a human can be identified using carpal and epiphysis bones ossification, which is limited to teen age. The accurate age estimation depends on best separation of bone pixels and soft tissue pixels in the ROI image. The traditional approaches like canny, sobel, clustering, region growing and watershed can be applied, but these methods requires proper pre-processing and accurate initial seed point estimation to provide accurate results. Therefore this paper proposes new approach to segment the bone from soft tissue and background pixels. First pixels are enhanced using BPE and the edges are identified by HIPI. Later a K-Means clustering is applied for segmentation. The performance of the proposed approach has been evaluated and compared with the existing methods.

show abstract

“…AI-Taani from Yarmouk University, Irbid, Jordan [44] have classified images according to age using point distribution model comprising of 120 images. For each bone a set of boundary points are chosen manually using mouse cursor by joining the existing points dynamically.…”

Section: Point Distribution Modelmentioning

confidence: 99%