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The lifespan can be increased in a positive mode by initial diagnosis of osteoporosis. Analysis of trabecular properties on digital hip radiographs could be useful in identifying the subjects with low bone mineral density (BMD) or osteoporosis. Early detection of fracture risk is important for initiating treatment and improving outcomes from both p...
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... falling come into picture. The architecture of the bone is composed by the cortical bone shell and trabecular bone core. The Trabecular bone is a spongy, porous type, found at the ends of all bones, such as pelvis and spine [6]. In proximal femur, trabecular bone forms a pattern of net-like strands varying in thickness and numbers [7] as shown in Fig. 1. It has a complex three dimensional structure consisting of struts and plates. Many lines of evidence indicate that the decreased bone strength which is the characteristic of osteoporosis is dependent not only on BMD, but also on trabecular bone micro-architecture and mineralization. The correlation between bone strength and bone mass ...
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Citations
... Dual X-ray Energy Absorptiometry (DEXA) is consideredto be the diagnostic method for fracture risk inthe medical field [13].Sapthagirivasan V. et al. [12] tested the energy of the bone. The result is compared with DEXA-BMD measurement of the proximal femur. ...
... Comparison of energy at neck by original and trabecular images [12] applied to pre-processed images and the above steps are repeated and energy values are compared. Results show that the pattern in the form of energy gave valuable information on the quality of the bones. ...
In this paper we present an a comparative analysis of a group of innovative image processing techniques that will enable early detection of osteoporosis. We proposed four distinct procedures that will enable the study of detection of osteoporosis through the study of MRI images and through the study of the dietary and lifestyle patterns of the subjects under consideration. Through these procedures we have invented techniques that will enable improvement of the study of images from Dual Energy X-beam Absorptiometry (DEXA) by using SVM classifiers. In another method osteoporosis conditions are revealed using fuzzification based prediction model. In the third work, Active Shape Model (ASM) based classification and identification of osteoporosis conditions have been proposed. In the fourth work, a comprehensive study of femur images for the identification and classification of osteoporosis has been implemented. Finally a comparative analysis of all these osteorporosis identification procedures have been done along with presentation of results.
... Dual X-ray Energy Absorptiometry (DEXA) is considered to be the diagnostic method for fracture risk in the medical field [13]. Sapthagirivasan V. et al. [12] tested the energy of the bone. The result is compared with DEXA-BMD measurement of the proximal femur. ...
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This research work presents a relative comparison of a multitude of novel medical image analysis procedures that is directed towards detecting osteomalacia-like conditions in women at an early stage of the disease. Four image analysis techniques have been invented for study of magnetic Resonanse Imaging (MRI) files. Apart from that an analysis of the food and daily life routine of the subjects have been carried out. The methodology of analysing DEXA images utilizing SVM classifiers have been improved by using the proposed methods. In another method have been developed that identifies condition related to osteomalacia by fuzzifying the model of predicting the disease. Int the next method a procedure to identify and classify osteomalacia in women have been developed using Active Shape Model (ASM). The fourth work is based upon identification of early stages of osteomalacia by analysis of femur images in women of varying age groups. In the end, a comparison of the developed techniques to identify osteomalacia have been carried out through submission of results.
... According to WHO prediction by the year 2050, India will have the highest number of LBM cases in the world [10]. In the evaluation of osteoporosis in aged population of both sexes, several semiquantitative radiographic methods were investigated, and it was reported that these methods can be used as an adjunct method for diagnosing this condition with good accuracy [11]. It was examined that, number of white pixel were lesser in the osteoporotic group when compared to normal group; on the other hand, number of black pixel were greater in LBM group, when compared to normal group [12]. ...
Low bone mass (LBM) is a universal health problem in which the bone becomes fragileand more frequent in women than the men. The objective was to evaluate the adequacy of the plain digital X-ray image of calcaneum for the low bone mass evaluation by implementing neural network with a feasible accuracy when compared to X-ray with dual energy absorptiometry. Here for the study purpose, total women studied (n=52, aged 30 years and above) were classified as follows: Group-I: Normal (n=26), Group-II: Women with LBM (n=26). In each subject, a X-ray was taken for right calcaneum lateral viewn. Also, we measured bone mineral density for right proximal femur by using DXA. X-ray image was processed in MATLAB tool. A semi-automatic technique is been employed for selecting the area with calcaneum, and its trabeculae features were extracted using Canny detection technique, shape features, texture analysis, and gray level co-occurrence matrix. The feature selection was done, based on high value (≥0.6) of measure of sample adequacy (MSA) of features using principal component analysis (PCA). The classification using selected features was done with the help of an artificial neural network (ANN). In women with LBM (Group-II), the mean values of number of white pixels, solidity and contrast of calcaneum were lesser significantly, when compared to the corresponding values measured in normal women (Group-I). A semi-automatic computer aided diagnosis (CAD) tool was developed to evaluate LBM from digital X-ray of calcaneum using ANN. The accuracy of the tool was found to be 94.2%, when compared to DXA. Hence, calcaneum X-ray can be used as a inexpensive technique for evaluation of LBM.
... In the evaluation of LBM, several bone densitometry techniques are readily available, which ranged from simple conventional X-ray based plain radiographic method to dedicated X-ray based equipment for quantitative assessment of bone mineral density (gcm-2 the characteristics of high accuracy and reproducibility, and low subject"s radiation dose while measurement, dual energy X-ray absorptiometry (DXA) is being considered as "gold" standard in the evaluation of LBM [3]. It can predict the risk of fracture and prevent from illness. ...
Low bone mass (LBM) is a universal health problem in which the bone becomes fragileand more frequent in women than the men. The objective was to evaluate the adequacy of the plain digital X-ray image of calcaneum for the low bone mass evaluation by implementing neural network with a feasible accuracy when compared to X-ray with dual energy absorptiometry. Here for the study purpose, total women studied (n=52, aged 30 years and above) were classified as follows: Group-I: Normal (n=26), Group-II: Women with LBM (n=26). In each subject, a X-ray was taken for right calcaneum lateral viewn. Also, we measured bone mineral density for right proximal femur by using DXA. X-ray image was processed in MATLAB tool. A semi-automatic technique is been employed for selecting the area with calcaneum, and its trabeculae features were extracted using Canny detection technique, shape features, texture analysis, and gray level co-occurrence matrix. The feature selection was done, based on high value (≥0.6) of measure of sample adequacy (MSA) of features using principal component analysis (PCA). The classification using selected features was done with the help of an artificial neural network (ANN). In women with LBM (Group-II), the mean values of number of white pixels, solidity and contrast of calcaneum were lesser significantly, when compared to the corresponding values measured in normal women (Group-I). A semi-automatic computer aided diagnosis (CAD) tool was developed to evaluate LBM from digital X-ray of calcaneum using ANN. The accuracy of the tool was found to be 94.2%, when compared to DXA. Hence, calcaneum X-ray can be used as a inexpensive technique for evaluation of LBM.
... They also analyzed the features with FFT analysis. In [176], Gabor filter and wavelet transform were used to extract the texture features. Wavelet transforms extracted the features in the form of energy and could be used to describe the quality of the bone for osteoporosis detection. ...
... The fusion of systems Osteoporosis depends on factors like age, gender, eating habits, and lifestyle. Many CAD sytems [172][173][174][175][176][177][178][179][180][181][182] have been developed on these factors and have shown good prediction results, but as the results are not guaranteed, so they cannot be used as sole diagnostic systems. If these factors like age and gender, which play very important role in the diagnosis of osteoporosis, are included in the vision-based CAD systems, they can give satisfying results. ...
Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture.
Graphical abstract
... The Trabecular bone is a spongy, porous type, found at the ends of all bones, such as pelvis and spine also this bone that has differentiate in thickness and numbers as shown in Figure 1. In proximal femur [15]. Osteoporosis occur when the features in trabecular pattern changed, they used fractal dimension and Gabor filter to extract this feature in earlier research. ...
... Trabecular Structure of The Femur[15]. ...
... Feature extraction using texture analysis and GLCM: Texture analysis attempts to quantify intuitive qualities described by terms such as rough and smooth as a function of the spatial variation in pixel intensities (entropy, range and standard deviation) features were used in this study [12]. Various statistical and GLCM features have been extracted such as energy, contrast, correlation, auto-correlation, homogeneity, and entropy. ...
Osteoporosis " is (low amount of bone mineral density, BMD) is one of the major health problem, which leads to bone fracture. It is more prevalent in women when compared to men. The objective was to investigate the usefulness of simple plain digital X-ray image of calcaneum in the evaluation of disease, when compared to dual energy X-ray absorptiometry (DXA) bone densitometer. In this study, total women studied were classified as follows: Group-I: Normal (n=21), Group-II: Low bone mass (n=21).In each subject, standard digital X-ray of calcaneum lateral view was taken; also, BMD of right side proximal femur was measured using DXA.The digital X-ray image was processed using a MATLAB tool. From the image, an area of 50×70 pixels of calcaneum, considered as region of interest (ROI) was cropped manually. Canny edge detection technique was performed to calculate both number of white-and black-pixels present in trabeculae of calcaneum. Further, texture analysis and feature extraction by grey level co-occurrence matrix (GLCM) were done. In women with low bone mass (Group-II), the mean values of number of white-, and black-pixels present trabeculae, standard deviation (SD) of trabaculae separation, standard deviation, and contrast of trabeculae were lesser statistically significant, when compared to corresponding mean values measured in normal group. Further, in all women studied, these features extracted from X-ray namely white pixel, black pixel, SD, and contrast were correlated statistically significant with BMD of proximal femur. Hence, simple calcaneum X-ray could be used as a simple process for the doctor to diagnose the disease in a cost effective way. Keywords—Low Bone Mass (LBM), Bone Mineral Density (BMD), dual energy X-ray absorptiometry(DXA), Texture features of calcaeum, Grey level co-occurrence matrix, MATLAB.
Osteoporosis disorder is exhibited by skeletal fractures in old age people. Ithas been developed into a serious health care issue in women. Low BMD (bone mineral density)showsprimary cause ofOsteoporosis.TheOsteoporosisdetection in the early stage is necessary to take precautionary steps. Here we have proposed a system to detectOsteoporosis in the rst stage. The input DXA images are considered; in the preprocessing steps, the viewed input image is resized and de-noised using the NLMF method. The preprocessed imagesare considered for segmentation. ROI segmented in the ROI, High energy, low energy, and low BMDfeatures are considered. The extracted features are trained using SVM classiers, and based on the features Osteoporosis affected, and non-affected is classied.
The Surgeon General estimates that by 2020, half of all Americans could have weak bones due to bone loss. Osteoporosis causes more than 1.5 million fractures every year. Identifying effective interventions based on individual patient characteristics remains a major challenge. Proximal femur fractures are common and devastating events for individuals with osteoporosis. Since fracture is primarily a mechanical event, noninvasive predictions of fracture strength and location would be useful both for identifying at-risk individuals and evaluating treatment effects. However, bone fracture prediction is complicated due to the complex microstructure and nanostructure of bone. Bone is a highly heterogeneous material with rate-dependent mechanical behavior and large inter-individual variation. In this study, we designed two mechanical test procedures to understand the mechanical response of bone under impact and quasi-static load tests. The boundary conditions of the tests were designed in a way to simulate a fall to the side. The present study consists of three main parts: cadaver testing, quantitative image analysis, and finite element (FE)modeling. We obtained ten human femur bones and used high-resolution CT to quantify the microstructure and density of each sample. Specimen-specific FE models were created to evaluate the ability of various failure criteria to predict experimental fracture. Afterward, the samples were tested and their failure patterns were recorded. The fractured samples were rescanned to analyze the fractured surfaces. Our experimental results show that the loading necessary to fracture the femur samples is much higher in the impact tests. However, the toughening mechanisms are more pronounced in quasi-static tests. We found that FE model formulations were able to accurately predict femur stiffness and strength for quasi-static and impact conditions separately, but that no single formulation could account for the rate-dependent outcomes.
Osteoporosis is a metabolic bone disorder characterized by low bone mass, degradation of bone micro-architecture and susceptibility to fracture. It is a growing major health concern across the world, especially in the elderly population. Osteoporosis can cause hip or spinal fractures that may lead to high morbidity and socio-economic burden. Therefore, there is a need for early diagnosis of osteoporosis and prediction of fragility fracture risk. In this paper, state-of-the-art and recent advances in imaging techniques for diagnosis of osteoporosis and fracture risk assessment have been explored. A review of segmentation methods used to segment the regions of interest and texture analysis methods used for classification of healthy and osteoporotic subjects are also presented. Furthermore, challenges posed by the current diagnostic tools have been studied and feasible solutions to circumvent the limitations are discussed. Early diagnosis of osteoporosis and prediction of fracture risk requires the development of highly precise and accurate low-cost diagnostic techniques that would help the elderly population in low economies.
