Performance Analysis of Clustering Algorithms for MRI Brain Images

Sumathi, A.; Kowsalya, K.; Srinithi, K.P.; Sudarvizhi, M.

doi:10.1109/iss1.2019.8907943

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2022

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“…To identify the strong and weak edge pixels, two thresholding operations are used. The weak borders of the pixels are removed [36][37][38]. Edges are discovered with Canny by removing noise from an image.…”

Section: Canny Edge Detectionmentioning

confidence: 99%

Non-Invasive Machine Learning-Based Classification of Bone Health

Bhise¹,

Havaldar²

2022

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Osteoporosis is a disease that affects both men and women of all ages but is more commonly seen in women. A measure called Bone Mineral Density (BMD) is often used to raise a warning about the disease. BMD is calculated using a variety of image processing algorithms in both X-ray and dual energy X-ray absorptiometry (DEXA) images. It is a measure of the important T-score, which reflects the degree of osteoporosis. There are many ways to quantify BMD, but DEXA is often regarded as the gold standard. The significance of DEXA images for osteoporosis detection was found in several research. The healthcare system has a serious issue with the lack of osteoporosis education and screening. There is a ton of literature available for diagnosing osteoporosis as well. The numerous methods for detecting osteoporosis will be covered in this review. The problems from the literature analysis, image processing algorithms for detecting osteoporosis, interpretations of the results, and potential recommendations are all included in this work.

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Section: Canny Edge Detectionmentioning

confidence: 99%

Non-Invasive Machine Learning-Based Classification of Bone Health

Bhise¹,

Havaldar²

2022

View full text Add to dashboard Cite

show abstract

Performance Analysis of Clustering Algorithms for MRI Brain Images

Cited by 1 publication

References 5 publications

Non-Invasive Machine Learning-Based Classification of Bone Health

Non-Invasive Machine Learning-Based Classification of Bone Health

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