Feature Selection of the Combination of Porous Trabecular with Anthropometric Features for Osteoporosis Screening

Sela, Enny Itje; Hartati, Sri; Harjoko, Agus; Wardoyo, Retantyo; Mudjosemedi, Munakhir

doi:10.11591/ijece.v5i1.pp78-83

Cited by 17 publications

(21 citation statements)

References 14 publications

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“…The condition of low bone mineral density may induce perforation on the trabecular bone of mandible that can be characterized by the increase of small areas in the radiograph. The result of this study is suitable for previous studies [10], [11], [14]. The previous study showed that periapical radiograph potential to be used as osteoporosis predictor using the parameter of length and the amount of strut on a particular region of the periapical radiograph.…”

Section: B Discussionmentioning

confidence: 51%

Measurement of Maximum Value of Dental Radiograph to Predict the Bone Mineral Density

Lestari¹,

Diqi²,

Widyaningrum³

2017

EECSI

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Post-menopausal woman has a high risk to have osteoporosis. The condition of osteoporosis is characterized by the bone mineral density. The gold standard of BMD examination is using DEXA scan, but it has a problem in high cost and limited availability. So the study about the alternative to overcome the problem is necessary. The objective of this study is to measure the maximum value of periapical radiograph and determine its ability to be a predictor for bone mineral density of lumbar spine and hip. Image processing method was applied to 37 data subject that involved periapical radiograph and DEXA scan. The grayscale image was converted into binary image to observe the connectivity of the pixels. Measurement of maximum value for each radiograph has been done and continued by linier regression method between the maximum value with the BMD of lumbar spine and hip. The result of this study showed that the maximum value has a weak correlation with the BMD of lumbar spine and hip. The maximum value also cannot be the predictor for BMD of lumbar spine and hip as the significant of F is larger than 0,05 in the linier regression test.

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Section: B Discussionmentioning

confidence: 51%

Measurement of Maximum Value of Dental Radiograph to Predict the Bone Mineral Density

Lestari¹,

Diqi²,

Widyaningrum³

2017

EECSI

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“…Regardless of whether bone mineral thickness (BMD) in subjects with a crack of the proximal femur (hip break) is not quite the same as that of age-coordinated controls is still discussed. This study [7] Main factors which are obtained by the combination of the porous trabecular schema along with the anthropometric highlights for osteoporosis screening. The examination test has their bone mineral thickness (BMD) estimated at the proximal femur/lumbar spine utilizing double vitality X-beam absorptiometry (DXA).…”

Section: A Causes Of Osteoporosismentioning

confidence: 99%

A Hybrid Classification and Prediction Methodology for the Diagonosis of Osteoporosis

Kumar¹,

Sharma²

2019

IJITEE

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With the advent of technology medical science is growing very fast . Disease diagonosis using machine learning technique is quite cumbersome. But efforts are made by the innovative minds to develope an optimal and efficient prediction model for the prediction of the disease viz, bone disease . Bone disease prediction is also a broad area of research where machine learning techniques can be used. Better prediction and analysis can be helpful to cure such disease . Osteoporosis is an osteo- metabolic disease characterized by low bone mineral density (BMD) and deterioration of the micro- architecture of the bone tissues, causing an increase in bone fragility and consequently leading to an increased risk of fractures.. Machine learning algorithms play important role for predicting and analyzing such disease using available algorithms and by modifying them . There are many algorithms such as SVM (Support vector machine), Genetic Algorithm, Naive bayes classifier and other tree based classifiers which are proposed in traditional scenario for prediction of expected data from an image. The goal of this paper is to discuss about a proposed hybrid analysis and prediction approach for diagonosing osteoporosis. This paper also discuss about various prediction models viz, svm prediction model as the efficient one while processing the textual data for symptoms analysis. Here symptoms are called predictors . A comparison using the Accuracy and training time is performed. The approach shows the efficiency of proposed model over textual data as well as graphical image data analysis.

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“…Osteoporotic bone alteration on the mandibular cortical bone is visible on panoramic radiograph and can be easily seen by a dentist. Whereas the mandible trabecular bone alterations related with osteoporosis also visible on periapical radiographs as well as panoramic radiographs [12], [16], [17]. The periapical radiography is a low-cost examination.…”

Section: Introductionmentioning

confidence: 99%

“…However, the result of the system may be influenced by observers' diagnostic performance [17]. Some previous studies showed that image analysis also can be applied in periapical radiograph to predict the osteoporosis condition [16]- [20]. The combination of BMD and texture parameter values reported to provide a better assessment of the fracture risk than that obtainable by BMD measurement only [21].…”

Section: Introductionmentioning

confidence: 99%

Image Analysis of Periapical Radiograph for Bone Mineral Density Prediction

Widyaningrum

Lestari

Jie

2018

IJECE

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<span>Osteoporosis is a systemic skeletal disease. Parameter from any bone site in the body has possibility to be developed as a predictor of osteoporosis. The alteration in the mandible trabecular bone is visible in periapical radiographs. The aim of this study was to correlate the area parameter and the integrated density of periapical radiograph with bone mineral density. Image analysis of periapical radiograph i.e. measurement of area parameter and integrated density was done on Region of Interest (ROI) by using canny edge detection method. Result of this study showed that the area parameter has asignificant (α<0.05) negative correlation with the bone mass density (BMD) of the lumbar spine (r = -0.371) and T-score of the lumbar spine (r = -0.383). The linear regression test showed that the area parameter only can be used to predict T-score of the lumbar spine (F=5.822, α<0.05). The integrated density showed a significant (α < 0.05) negative correlation with T-score of hip (r = -0.332) and T-score of lumbar spine (r = -0.377). It can be concluded that the area parameter can be used as one of input parameters for computer-aided system of osteoporosis early detection by using periapical radiograph.</span>

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Feature Selection of the Combination of Porous Trabecular with Anthropometric Features for Osteoporosis Screening

Cited by 17 publications

References 14 publications

Measurement of Maximum Value of Dental Radiograph to Predict the Bone Mineral Density

Measurement of Maximum Value of Dental Radiograph to Predict the Bone Mineral Density

A Hybrid Classification and Prediction Methodology for the Diagonosis of Osteoporosis

Image Analysis of Periapical Radiograph for Bone Mineral Density Prediction

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