Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches.

OBJECTIVES: This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis. METHODS: The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers. RESULTS: Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942-0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD. CONCLUSIONS: The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage.

Statistical quantification of the effects of viewing distance on texture perception.

In general, viewers are more attracted to local features in images at a shorter viewing distance and to global features in images at a longer viewing distance. However, numerical analysis of the effect of viewing distance on human texture perception and how the perception of global and local changes under certain conditions are still undetermined. In this paper, we present statistical prediction of the relationship between the domination ratio of global and local features and the viewing distances under the control of several factors, using the logistic regression model. We synthesized textures by separately controlling global and local textural features using a texture model based on mathematical morphology, namely the primitive, grain, and point configuration texture model. Visual sensory tests were carried out on 80 subjects during two sets of experiments. The collected data were statistically analyzed using logistic regression and Akaike information criteria. Besides the main factor of viewing distance, the factors including gender, changing the order of viewing positions, and prior knowledge were also shown quantitatively to have significant influence on human texture perception. Our results showed that (1) local features of a texture were more attractive to females than males, (2) the first impression might have affected subsequent decisions in texture perception, and (3) subjects who had prior knowledge (supervised) were more sensitive to the changes in global and local dominance. (4) Regarding the interactions of the factors, prior knowledge reduced the effects of individual differences and perception condition differences on human texture perception. This study is dedicated to the construction of numerical relationships between viewing distance and human texture perception as well as to cognitive investigation of biases in global and local perceptions.

The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis.

PURPOSE: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. MATERIALS AND METHODS: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. RESULTS: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. CONCLUSION: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

Computer-aided measurement of mandibular cortical width on dental panoramic radiographs for identifying osteoporosis.

AIM: To develop a computer-aided diagnosis system to continuously measure mandibular inferior cortical width on dental panoramic radiographs and evaluate the system's efficacy in identifying postmenopausal women with low-skeletal bone mineral density. METHODS: Mandibular inferior cortical width was continuously measured by enhancing the original X-ray image, determining cortical boundaries, and evaluating all distances between the upper and lower boundaries in the region of interest. The system's efficacy in identifying osteoporosis at the lumbar spine and the femoral neck was evaluated for 100 women (≥50 years): 50 in the development of the tool and 50 in its validation. RESULTS: The sensitivity and specificity of the cortical measurements for identifying the development patients were 90% (95% confidence interval shown in parentheses) (63.0-87.0) in women with low spinal bone mineral density, and 81.8% (70.1-91.8) and 69.2% (56.2-81.8) in those with low femoral bone mineral density, respectively. Corresponding values in the validation patients were 93.3% (85.9-100) and 82.9% (71.4-92.7) at the lumbar spine, and 92.3% (84.5-99.5) and 75.7% (63.0-87.0) at the femoral neck, respectively. CONCLUSION: Our new computer-aided diagnosis system is a useful procedure in triage screening for osteoporosis.

Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system.

BACKGROUND: Early diagnosis of osteoporosis can potentially decrease the risk of fractures and improve the quality of life. Detection of thin inferior cortices of the mandible on dental panoramic radiographs could be useful for identifying postmenopausal women with low bone mineral density (BMD) or osteoporosis. The aim of our study was to assess the diagnostic efficacy of using kernel-based support vector machine (SVM) learning regarding the cortical width of the mandible on dental panoramic radiographs to identify postmenopausal women with low BMD. METHODS: We employed our newly adopted SVM method for continuous measurement of the cortical width of the mandible on dental panoramic radiographs to identify women with low BMD or osteoporosis. The original X-ray image was enhanced, cortical boundaries were determined, distances among the upper and lower boundaries were evaluated and discrimination was performed by a radial basis function. We evaluated the diagnostic efficacy of this newly developed method for identifying women with low BMD (BMD T-score of -1.0 or less) at the lumbar spine and femoral neck in 100 postmenopausal women (≥50 years old) with no previous diagnosis of osteoporosis. Sixty women were used for system training, and 40 were used in testing. RESULTS: The sensitivity and specificity using RBF kernel-SVM method for identifying women with low BMD were 90.9% [95% confidence interval (CI), 85.3-96.5] and 83.8% (95% CI, 76.6-91.0), respectively at the lumbar spine and 90.0% (95% CI, 84.1-95.9) and 69.1% (95% CI, 60.1-78.6), respectively at the femoral neck. The sensitivity and specificity for identifying women with low BMD at either the lumbar spine or femoral neck were 90.6% (95% CI, 92.0-100) and 80.9% (95% CI, 71.0-86.9), respectively. CONCLUSION: Our results suggest that the newly developed system with the SVM method would be useful for identifying postmenopausal women with low skeletal BMD.

Claudins in Caenorhabditis elegans: their distribution and barrier function in the epithelium.

Claudins ( approximately 23 kDa) with four transmembrane domains are major cell adhesion molecules working at tight junctions in vertebrates, where the intercellular space is tightly sealed (reviewed in ). We examined here the possible occurrence of claudin-like proteins in invertebrates, which do not bear typical tight junctions. Close blast searching of the C. elegans genome database identified four claudin-related, approximately 20-kDa integral membrane proteins (CLC-1 to -4), which showed sequence similarity to the vertebrate claudins. The expression and distribution of CLC-1 was then examined in detail by GFP technology as well as by immunofluorescence microscopy. CLC-1 was mainly expressed in the epithelial cells in the pharyngeal region of digestive tubes and colocalized with AJM-1 at their intercellular junctions. Then, to examine the possible involvement of CLC-1 in the barrier function, we performed RNA interference in combination with a tracer experiment: in CLC-1-deficient worms, the barrier function of the pharyngeal portion of the digestive tubes appeared to be severely affected. CLC-2 was expressed in seam cells in the hypodermis, and it also appeared to be involved in the hypodermis barrier. These findings indicated that multiple species of the claudin homologs, which are involved in the barrier function of the epithelium, exist in C. elegans.