IDENTIFICATION OF BONE DENSITY LEVEL USING THE GAUSSIAN MIXTURE MODELS – EXPECTATION MAXIMIZATION (GMM-EM) METHOD

Agung Efendy, I Komang Somawirata, Irmalia Suryani Faradisa

Abstract


In the human body, there are bones that support the body. These bones have a certain level of density. The level of bone density or Bone Mineral Density (BMD) is divided into three parts: normal bone, osteopenia, and osteoporosis. The author intends to develop this research by applying the Gaussian Mixture Models (GMM) method because previous studies tend not to classify BMD into three types, only referring to the diagnosis of osteoporosis alone, and is expected to obtain results with a high level of accuracy. The GMM algorithm method used in this study is a method for obtaining distributed segmentation results through estimation preprocessing that produces results by maximizing the likelihood function. Expectation Maximization (EM) is a clustering algorithm based on the use of probability calculation models to achieve high expectations and maximization.


Keywords


Bones, Bone Mineral Density (BMD), Gaussi-an Mixture Models (GMM), Expectation Maximization (EM)

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References


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DOI: https://doi.org/10.29100/jipi.v11i2.10675

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