Detection of Osteoporosis in X-Ray image data (original) (raw)
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Proceedings of the 2019 InSITE Conference
Aim/Purpose: The aim of the study was to analyze the structure of the bone tissue by using texture analysis of the bone trabeculae, as visualized in a routine radiograph of the proximal femur . This could provide objective information regarding both the mineral content and the spatial structure of bone tissue. Therefore, machine-learning tools were applied to explore the use of texture analysis for obtaining information on the bone strength. Background: One in three women in the world develops osteoporosis, which weakens the bones, causes atraumatic fractures and lowers the quality of life. The damage to the bones can be minimized by early diagnosis of the disease and preventive treatment, including appropriate nutrition, bone-building exercise and medications. Osteoporosis is currently diagnosed primarily by DEXA (Dual Energy X-ray Absorptiometry), which measures the bone mineral density alone. However, bone strength is determined not only by its mineral density but also by the spa...
Basic study for automatic recognition of osteoporosis using abdominal x-ray CT images
Medical Imaging 2004: Image Processing, 2004
We have developed an algorithm that can be used to distinguish the central part of the vertebral body from an abdominal X-ray CT image and to automatically calculate three measures to diagnose the degree of osteoporosis in a patient. In addition, we examined whether it is possible to use these CT images as an aid in diagnosing osteoporosis. Three measures that were automatically extracted from the central part of a vertebral body in the CT images were compared with the bone mineral density (BMD) values that were obtained from the same vertebral body. We calculated the mean CT number, coefficient of variation, and the first moment of power spectrum in the recognized vertebral body. We judged whether a patient had osteoporosis using the diagnostic criteria for primary osteoporosis (Year 2000 revision, published by the Japanese Society for Bone and Mineral Research). We classified three measures for normal and abnormal groups using the principal component analysis, and the two groups were compared with the results obtained from the diagnostic criteria. As a result, it was found that the algorithm could be used to distinguish the central part of the vertebral body in the CT images and to calculate these measures automatically. When distinguishing whether a patient was osteoporotic or not with the three measures obtained from the CT images, the ratio (sensitivity) usable for diagnosing a patient as osteoporotic was 0.93 (14/15), and the ratio (specificity) usable for diagnosing a patient as normal was 0.64 (7/11). Based on these results, we believe that it is possible to utilize the measures obtained from these CT images to aid in diagnosing osteoporosis.
Localization of Disease Osteoporosis in X-ray Images
International Journal for Research in Applied Science and Engineering Technology
People with osteoporosis are more likely to break bones, particularly in the wrist, hip, and spine. Determining the existence of osteoporosis requires measurements of bone quantity and quality, especially bone mineral density (BMD). Science may use many techniques in order to ascertain the BMD. Dual Energy X-ray Absorptiometry (DEXA) is one of the procedures that has gained the most widespread acceptance. The T-score is a method that uses bone mineral density to quantify the degree to which osteoporosis has progressed (BMD). The BMD assessment may be seen on X-ray or DEXA images. Bone mineral density (also known as BMD) is tested in order to make a diagnosis of osteoporosis. This article offers an overview of many popular image-processing methods used in BMD assessment. These methods include image augmentation, segmentation, and texture analysis. Due to its many advantages, DEXA is finding a wide variety of new applications in medicine and science. At the end of the piece, we take a quick look at the first techniques for determining BMD. Similarities between DEXA and X-ray images are also highlighted in the article. The methods of image processing that may be used to detect osteoporosis are detailed in the article. Methods for preparing X-ray and DEXA pictures for analysis, extracting features from those images, and segmenting them are described.
XSITRAY: A Database for the Detection of Osteoporosis Condition
Biomedical and Pharmacology Journal
In the medical era, health of a bone is accessed by the bone mineral density (BMD) test. Bone fracture risk in the humans are estimated or evaluated by the BMD test. The test statement recognizes the presence of signs of presence of the frequent occurring disease in the bone called as osteoporosis. In the earlier stage, the challenge in the BMD measurement is that traditional x-rays are used with a step wedge made from an aluminum or ivory phantom. At each step of the phantom with the known densities, bone content present is intended by a illustration assessment of the density present in the bone. Effectiveness in the value and feasibility in the X-rays compared to cutting-edge methods divulge the potential for novel medical relevance among the investigators. So it is obligatory to enclose a customary database in X-Ray images for the young bud researchers to capture up the dealings to the advance stage by accurate examination of the medical results of the images. The projected X-Ray...
Osteoporosis is a disease characterized by impairment of bone microarchitecture that causes high socioeconomic impacts in the world because of fractures and hospitalizations. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing the disease, access to DXA in developing countries is still limited due to its high cost, being present only in specialized hospitals. In this paper, we analyze the performance of Osseus, a low-cost portable device based on electromagnetic waves that measures the attenuation of the signal that crosses the medial phalanx of a patient's middle finger and was developed for osteoporosis screening. The analysis is carried out by predicting changes in bone mineral density using Osseus measurements and additional common risk factors used as input features to a set of supervised classification models, while the results from DXA are taken as target (real) values during the training of the machine learning algorithms. The dataset cons...
International Journal of Environmental Research and Public Health, 2021
Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with traditional prediction models. Data were acquired from community-dwelling participants enrolled in health checkup programs at a medical center in Taiwan. A total of 3053 men and 2929 women were included. Models were constructed for men and women separately with artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LoR) to predict the presence of osteoporosis. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. We achieved AUROC of 0.837, 0.840, 0.843, 0.821, 0.827 in men, and 0.781, 0.807, 0.811, 0.767, 0.772 in women, for ANN, SVM, RF, KNN, and LoR models...
Osteoporosis detection using machine learning techniques and feature selection
Osteoporosis is a disease of bones that leads to an increased risk of fracture and it is characterized by low bone mineral density and micro-architectural deterioration of bone tissue. In this article, the dataset consists of 3426 subjects (1083 pathological and 2343 healthy cases) whose diagnosis was based on laboratory and osteal bone densitometry examination. In all cases, four diagnostic factors for osteoporosis risk prediction, namely age, sex, height and weight were stored for later evaluation with the selected classifiers. In order to categorize subjects into two classes (osteoporosis, non-osteoporosis), twenty machine learning techniques were assessed, based on their popularity and frequency in biomedical engineering problems. All classifiers have been evaluated using the well-known 10-fold cross validation method and the results are reported analytically. In addition, a feature selection method identified that with the use of only two diagnostic factors (age and weight), similar performance could be achieved. The scope of the proposed exhaustive methodology is to assist therapists in osteoporosis prediction, avoiding unnecessary further testing with bone densitometry.
Indonesian Journal of Electrical Engineering and Computer Science, 2022
Osteoporosis is one of the most common diseases that affect the bones of adults, especially women in menopause, and the reason for this is due to the lack of bone mineral density bone mineral density (BMD). BMD can be measured by X-ray and dual energy X-ray absorptiometry (DEXA) images, this article, focused on using DEXA images for Osteoporosis detection. At first, the original image must passed through the preprocessing stage, during which the noisy parts is reduced, and the useless parts are eliminated, and then the contrast between adjacent areas is increased and the area of interest is allocated. After that, the image is passed in a deep learning model in order to extract the unique features on the basis of which each image is classified. The classification result was excellent with 98% accuracy. The used dataset is "Osteoporosis DEXA scans images" of Spine from Pakistan.
Background: Lifespan and its quality can be improved by early diagnosis of osteoporosis. Analysis of trabecular boundness on digital hip radiographs could be useful for identifying subjects with low bone mineral density (BMD) or osteoporosis. The main aim of our study was to evaluate the ability of a kernelbased support vector machine (SVM) with respect to diagnosis and add to knowledge about the trabecular features of digital hip radiographs for identifying subjects with low BMD. Method: In this paper we present an SVM kernel classifier-based computer-aided diagnosis (CAD) system for osteoporotic risk detection using digital hip radiographs. Initially, the original radiograph was intensified, then trabecular features such as boundness, orientation, solidity of spur and delta were evaluated and radial bias function (RBF) based discrimination was manifested. The next step was the evaluation of the diagnostic capability of the proposed method in order to spot subjects with low BMD at the femoral neck in 50 (50.7714.3 years) South Indian women with no previous history of osteoporotic fracture. Out of 50 subjects, 28 were used to train the classifier and the other 22 were used for testing. Results: The proposed system has achieved the highest classification accuracy documented so far by means of a fivefold cross-validation analysis with mean accuracy of 90% (95% confidence interval (CI): 82 to 98%); sensitivity and positive predictive value (PPV) were 90% (95% CI: 82 to 98%) and 89% (95% CI: 81 to 97%), respectively. Pearson's correlation was observed at the level of po0.001, between extracted image trabecular features with age and BMDs measured by dual energy x-ray absorptiometry (DXA). Extracted image features also demonstrated significant differences between high and low BMD groups at the level of p<0.001. Conclusion: Our findings suggest that the proposed CAD system with SVM would be useful for spotting women vulnerable to osteoporotic risk.