Automated multiple lesion identification on vertebral spine using modified average intensity

Sungkhun, Sukonthee; Keo, Chea; Khoeun, Ratanak; Chinnasarn, Krisana; Rasmequan, Suwanna; Rodtook, Annupan

doi:10.1109/icaicta.2016.7803125

Cited by 2 publications

(1 citation statement)

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“…Sungkhun et al utilized x-ray images to categorize diseases such as Osteopenia and osteoporosis of the spine by five major steps. 41 Automatic Cropping of the spine image in the x-Ray and locating lumbar spine using segmentation with the help of normal distribution and vertical projection. Distance regularized intensity position progress and Gamma corrections (GC) are used for contour identification.…”

Section: Korchiyne Et Al Proposed Machine Learning-basedmentioning

confidence: 99%

Detection of osteoporosis in lumbar spine [L1-L4] trabecular bone: a review article

2021

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<p class="abstract">The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy where as the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. This analysis is on the basis of bone mineral density (BMD) standards obtained through a variety of scientific methods experimented from different skeletal regions. The detection of osteoporosis in lumbar spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. This paper focuses on the advanced technology in imaging systems and fracture probability analysis of osteoporosis detection. The various segmentation techniques are explored to examine osteoporosis in particular region of the image and further significant attributes are extracted using different methods to classify normal and abnormal (osteoporotic) bones. The limitations of the reviewed papers are more in feature dimensions, lesser accuracy and expensive imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and DEXA. To overcome these limitations it is suggested to have less feature dimensions, more accuracy and cost-effective imaging modality like X-ray. This is required to avoid bone fractures and to improve BMD with precision which further helps in the diagnosis of osteoporosis.</p>

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Section: Korchiyne Et Al Proposed Machine Learning-basedmentioning

confidence: 99%