Prospective assessment of bone texture parameters at the hand in rheumatoid arthritis

Evaluation of mandibular condyle trabecular structure in patients with rheumatoid arthritis using fractal analysis

OBJECTIVES

The aim of this study was to compare the fractal dimension (FD) of trabecular structure of the mandibular condyles in patients with rheumatoid arthritis (RA) to patients without RA. Correlations between condylar FD and bone mineral density T-scores in the femoral neck and lumbar spine were also examined.

STUDY DESIGN

The RA study group patients were divided into 3 categories (33 normal, 33 osteopenic, and 34 osteoporotic) according to T-scores. The control group without RA was sex- and age-matched with the study group. FD was calculated from panoramic radiographs and compared between the study and control groups. The relationships between FD values and femoral neck and L1-L4 lumbar spine T-scores were investigated for study and control groups. Significance was established at P < .05.

RESULTS

The mean FD values of the entire study group and of each category in the study group were significantly lower than those of the control group (P < .001). There were no significant differences in FD values among the 3 RA categories (P > .05). No significant correlations appeared between FD and femoral neck or lumbar spine T-scores (P ≥ .063).

CONCLUSIONS

Fractal analysis of the condyles on panoramic radiographs can distinguish RA from healthy condyles, even if the patients with RA have normal bone mineral density T-scores.

Automated selection of bone texture regions on hand radiographs: Data from the Osteoarthritis Initiative

Manual selection of finger trabecular bone texture regions on hand X-ray images is time-consuming, tedious, and observer-dependent. Therefore, we developed an automated method for the region selection. The method selects square trabecular bone regions of interest above and below the second to fifth distal and proximal interphalangeal joints. Two regions are selected per joint (16 regions per hand). The method consists of four integral parts: (1) segmentation of a radiograph into hand and background, (2) identification of finger regions, (3) localization of center points of heads of distal phalanges and the distal interphalangeal, proximal interphalangeal, and metacarpophalangeal joints, and (4) placement of the regions of interest under and above the distal and proximal interphalangeal joints. A gold standard was constructed from regions selected by two observers on 40 hand X-ray images taken from Osteoarthritis Initiative cohort. Datasets of 520 images were generated from the 40 images to study the effects of hand and finger positioning. The accuracy in regions selection and the agreement in calculating five directional fractal parameters were evaluated against the gold standard. The accuracy, agreement, and effects of hand and finger positioning were measured using similarity index (0 for no overlap and 1 for entire overlap) and interclass correlation coefficient as appropriate. A high accuracy in selecting regions (similarity index ≥ 0.79) and a good agreement in fractal parameters (interclass correlation coefficient ≥ 0.58) were achieved. Hand and finger positioning did not affect considerably the region selection (similarity index ≥ 0.70). These results indicate that the method developed selects bone regions on hand X-ray images with accuracy sufficient for fractal analyses of bone texture.

Directional fractal signature methods for trabecular bone texture in hand radiographs: data from the Osteoarthritis Initiative

PURPOSE

To develop directional fractal signature methods for the analysis of trabecular bone (TB) texture in hand radiographs. Problems associated with the small size of hand bones and the orientation of fingers were addressed.

METHODS

An augmented variance orientation transform (AVOT) and a quadrant rotating grid (QRG) methods were developed. The methods calculate fractal signatures (FSs) in different directions. Unlike other methods they have the search region adjusted according to the size of bone region of interest (ROI) to be analyzed and they produce FSs defined with respect to any chosen reference direction, i.e., they work for arbitrary orientation of fingers. Five parameters at scales ranging from 2 to 14 pixels (depending on image size and method) were derived from rose plots of Hurst coefficients, i.e., FS in dominating roughness (FSSta), vertical (FSV) and horizontal (FSH) directions, aspect ratio (StrS), and direction signatures (StdS), respectively. The accuracy in measuring surface roughness and isotropy/anisotropy was evaluated using 3600 isotropic and 800 anisotropic fractal surface images of sizes between 20 × 20 and 64 × 64 pixels. The isotropic surfaces had FDs ranging from 2.1 to 2.9 in steps of 0.1, and the anisotropic surfaces had two dominating directions of 30° and 120°. The methods were used to find differences in hand TB textures between 20 matched pairs of subjects with (cases: approximate Kellgren-Lawrence (KL) grade ≥ 2) and without (controls: approximate KL grade <2) radiographic hand osteoarthritis (OA). The OA Initiative public database was used and 20 × 20 pixel bone ROIs were selected on 5th distal and middle phalanges. The performance of the AVOT and QRG methods was compared against a variance orientation transform (VOT) method developed earlier [M. Wolski, P. Podsiadlo, and G. W. Stachowiak, "Directional fractal signature analysis of trabecular bone: evaluation of different methods to detect early osteoarthritis in knee radiographs," Proc. Inst. Mech. Eng., Part H 223, 211-236 (2009)].

RESULTS

The AVOT method correctly quantified the isotropic and anisotropic surfaces for all image sizes and scales. Values of FSSta were significantly different (P < 0.05) between the isotropic surfaces. Using the VOT and QRG methods no differences were found at large scales for the isotropic surfaces that are smaller than 64 × 64 and 48 × 48 pixels, respectively, and at some scales for the anisotropic surfaces with size 48 × 48 pixels. Compared to controls, using the AVOT and QRG methods the authors found that OA TB textures were less rough (P < 0.05) in the dominating and horizontal directions (i.e., lower FSSta and FSH), rougher in the vertical direction (i.e., higher FSV) and less anisotropic (i.e., higher StrS) than controls. No differences were found using the VOT method.

CONCLUSIONS

The AVOT method is well suited for the analysis of bone texture in hand radiographs and it could be potentially useful for early detection and prediction of hand OA.