Holger F. Boehm

Osteoporosis is a chronic condition characterized by demineralization and destruction of bone tissue. Fractures associated with the disease are becoming an increasingly relevant issue for public health institutions. Prediction of fracture risk is a major focus research and, over the years, has been approched by various methods. Still, bone mineral density (BMD) obtained by dual-energy X-ray absorptiometry (DXA) remains the clinical gold-standard for diagnosis and follow-up of osteoporosis. However, DXA is restricted to specialized diagnostic centers and there exists considerable overlap in BMD results between populations of individuals with and without fractures. Clinically far more available than DXA is conventional x-ray imaging depicting trabecular bone structure in great detail. In this paper, we demonstrate that bone structure depicted by clinical radiographs can be analysed quantitatively by parameters obtained from the Radon Transform (RT). RT is a global analysis-tool for detection of predefined, parameterized patterns, e.g. straight lines or struts, representing suitable approximations of trabecular bone texture. The proposed algorithm differentiates between patients with and without fractures of the hip by application of various texture-metrics based on the Radon-Transform to standard x-ray images of the proximal femur. We consider three different regions-of-interest in the proximal femur (femoral head, neck, and inter-trochanteric area), and conduct an analysis with respect to correct classification of the fracture status. Performance of the novel approach is compared to DXA. We draw the conclusion that performance of RT is comparable to DXA and may become a useful supplement to densitometry for the prediction of fracture risk.

OBJECTIVE. We used an algorithm for quantitative image processing to classify breast tissue into the categories fibrosis, involution atrophy, and normal. The algorithm entailed use of Minkowski functionals in topologic analysis of x-ray attenuation patterns on digital mammograms. The results were compared with those of techniques based on evaluation of gray-level histograms.MATERIALS AND METHODS. One hundred digital mammograms were classified by consensus of two experienced readers. A topologic parameter extracted from the Minkowski functional spectra was obtained for retromammilar image sections (512 × 512 pixels). From the gray-level histogram of each of these samples, the 20th percentile, median, and mean were determined. Discriminant analysis was used to assess the predictive value of the methods with respect to correct categorization.RESULTS. The mean gray-level intensity of normal breast tissue was 90 ± 9, and the 20th percentile was 68 ± 18. The mean gray-level intensity was 84 ± 7 for involution a...

To provide a novel, robust algorithm for classification of lung tissue depicted by multi-detector computed tomography (MDCT) based on the topology of CT-attenuation values and to compare discriminative results with densitometric methods. Two hundred seventy-five cubic volumes of interest (VOI, edge length 40 pixels) were obtained from MDCT chest CT (isotropic voxel size, edge length 0.6 mm) of 21 subjects with and without pathology (emphysema, fibrosis). All VOIs were visually consensus-classified by two radiologists. Texture features based on the Minkowski functionals (MF) as well as on the CT attenuation values are determined. Classification results of both approaches were assessed by receiver-operator characteristic and discriminant analysis. By densitometric (topological) parameters, normal and abnormal VOIs were distinguished with an area under the curve ranging from 0.78 to 0.85 (0.87 to 0.96). Correlation between both groups of parameters was non-significant (p ≥ 0.36). By combined information of densitometric and topological quantities, the radiologists’ ratings were reproduced for 92% of VOIs, ranging from 85.7% (fibrosis) to 98% (normal VOIs). Our method performs well for identification of VOIs containing abnormal lung-tissue. Combined information of densitometry and topology increases the number of correctly classified VOIs further. When extended to CT data depicting whole lungs, topological analysis may allow to enhance density-based analysis and improve monitoring texture changes with progression of pulmonary disease.

Summary Texture features based on the Radon transform were extracted from clinical radiographs of the hip in post-menopausal women. The novel algorithm allowed us to identify patients with fracture of the proximal femur and may provide an alternative to measuring bone mineral density in predicting the fracture-risk in osteoporosis, especially where densitometry is regionally unavailable. Introduction The aim of this study is to introduce an algorithm for differentiation between patients with and without fracture of the hip using parameters based on the Radon transform (RT) and applied to standard radiographs of the proximal femur and to compare the results with bone mineral density (BMD). Methods The study comprised 50 post-menopausal women (78.6 ± 11.5 years of age), including 25 patients with hip fracture and 25 age-matched controls. We obtained lumbar and femoral BMD and standard femoral radiographs. In the radiographs we analysed trabecular patterns of the hip in a region-of-interest of 57 x 29 mm using the RT. From the histogram-representation of the RT, we extracted several characteristic parameters. By ROC and discriminant-analysis, we assessed the statistical power of both methods. Results For correct differentiation between fracture and non-fracture cases by femoral BMD, area-under-the-curve (AUC) was 0.78; AUC for the RT-based parameters ranged from 0.73 to 0.8. By combination of densitometric and textural information in a multivariate model the fracture status of 84% of subjects was predicted correctly, identification of fracture cases rose to 88%. Conclusion Identification of fracture patients by RT applied to femoral radiographs was feasible and seemed to have a discriminative potential comparable to that of standard densitometry. In the future, the new method may provide an alternative to DXA or in conjunction with conventional densitometry may enhance the detection of patients with elevated risk of hip fracture.

With an estimated life-time-risk of about 10%, breast cancer is the most common cancer among women in western societies. Extensive mammography-screening programs have been implemented for diagnosis of the disease at an early stage. Several algorithms for computer-aided detection (CAD) have been proposed to help radiologists manage the increasing number of mammographic image-data and identify new cases of cancer. However, a major issue with most CAD-solutions is the fact that performance strongly depends on the structure and density of the breast tissue. Prior information about the global tissue quality in a patient would be helpful for selecting the most effective CAD-approach in order to increase the sensitivity of lesion-detection. In our study, we propose an automated method for textural evaluation of digital mammograms using the Minkowski Functionals in 2D. 80 mammograms are consensus-classified by two experienced readers as fibrosis, involution/atrophy, or normal. For each case, the topology of graylevel distribution is evaluated within a retromamillary image-section of 512 x 512 pixels. In addition, we obtain parameters from the graylevel-histogram (20th percentile, median and mean graylevel intensity). As a result, correct classification of the mammograms based on the densitometic parameters is achieved in between 38 and 48%, whereas topological analysis increases the rate to 83%. The findings demonstrate the effectiveness of the proposed algorithm. Compared to features obtained from graylevel histograms and comparable studies, we draw the conclusion that the presented method performs equally good or better. Our future work will be focused on the characterization of the mammographic tissue according to the Breast Imaging Reporting and Data System (BI-RADS). Moreover, other databases will be tested for an in-depth evaluation of the efficiency of our proposal.

We introduce an algorithm to evaluate hip DXA scans using quantitative image analysis procedures based on the Minkowski functionals (MF) for differentiation between post-menopausal women with and without hip fracture. In a population of 30 post-menopausal women, the new parameter has a highly discriminative potential with a performance superior to standard densitometry providing complementary information compared to BMD. We introduce a novel algorithm to evaluate DXA scans of the hip using quantitative image analysis based on the Minkowski functionals (MF) to identify post-menopausal women with hip-fracture and to compare the results with densitometry. BMD of 30 women (73.9 ± 10.3 years), 15 of whom had a recent hip fracture, is obtained by DXA using the “total hip” ROI. The topology of mineral distribution in the scan images is evaluated using the MF-based parameter MF2D. ROC analysis is employed to assess the discriminative potential (fracture/non-fracture). The area-under-the-curve (AUC) for identification of patients with/without fractures by BMD is .72(p = 0.04), AUC for MF2D is .85(p = 0.001). No statistically significant correlation exists between MF2D and BMD. By discriminant analysis we can show that by combination of MF2D and BMD the outcome increases significantly: using BMD or MF2D alone, 63% and 70% of cases are classified correctly versus 77% of cases in the multivariate model. The topology-based parameter has a high predictive potential with respect to identification of patients with high risk of hip fracture, performance is superior to densitometry. The new method provides information complementary to BMD. Best classification results are obtained when BMD and MF2D are combined in a multivariate model.