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PURPOSE The diagnosis of most rotator cuff tears (RCTs) relies upon magnetic resonance (MR) imaging, but direct capture of MR images without enhanced image processing leads to poor image contrast and potential misdiagnosis. Therefore, we developed a 2-stage model for the detection and diagnosis of injury of the supraspinatus tendon. METHODS The first stage used coupled weighted histogram separation (WHS) to improve image enhancement, and the second stage extracted suspicious texture, features of both spatial and spectral domains, and sequential floating forward selection (SFFS) selected features conducive to classification of RCTs. We then tested injuries of the supraspinatus tendon using the classifier. RESULTS The extraction of features by SFFS can increase detection of supraspinatus injury by reducing the input vector by 57.78% from the enhanced input images. The receiver operating characteristic (ROC) curve indicated an azimuth (Az) value of 84.38% when SFFS selected 76 features to construct a support vector machine (SVM) classifier from the enhanced images, compared with 56.94% when all 180 features from the raw input images were used for the construction. CONCLUSIONS The performance of the classifier constructed by SFFS-selected features is superior to that using all features. These findings can serve as references to improve diagnosis and treatment of supraspinatus injuries.

Magnetic Resonance images are used to identify tears in the meniscus, a debilitating condition of the knee. In this paper, a two-staged computerized system for diagnosing meniscus tears is proposed. In the first stage, we apply an Active Contour with Level Sets model to calculate the location and shape of the meniscus area. In the second stage, 180 features, consisting of textural information in the spatial and spectral domains, were extracted from each suspected meniscus tear. Sequential floating forward selection (SFFS) was applied to select the relevant features. The feature vectors were then input to a support vector machine (SVM) classifier to detect meniscus tears. The Az value of the receiver operating characteristic (ROC) curve were used to evaluate the classification performance. Experimental results show that the dimension of the feature vector was reduced from 180 to 64 after the SFFS. Meanwhile, the SVM classifier combined with the SFFS feature selection (Az = 0.9123) outperformed the SVM clas...

Purpose: Magnetic Resonance Imaging is the most common tool used to diagnose supraspinatus muscle rupture or tear. However, discriminating the features between the rupture and the tear in MR images is difficult, and leads to low diagnostic accuracy. In this article, image enhancement techniques are performed on the original images to improve diagnostic performance. Methods: Three types of histogram-based image enhancement techniques are employed to enhance rotor cuff MR images: (1) histogram equalization; (2) weighted histogram separation (WHS); and (3) coupled WHS. The peak signal-to-noise ratio (PSNR) was used to evaluate the quality of the enhanced images. The experimental group comprises the enhanced images with the highest PSNR and the original MR images. The control group comprises only the original MR images. Both the control group and experimental group were randomly presented to three orthopedic physicians for performance evaluation. The accuracy rate and F-measure were applied to measure the ave...

Background: A decline in cerebral White Matter (WM) integrity occurs across the adult lifespan, with more rapid degradation later in life. Diffusion Tensor Imaging (DTI) is a non-invasive method to evaluate the microscopic changes of WM in vivo. Linear regression models have been applied to depict the relationship between diffusion properties, such as Fractional Anisotropy (FA) and Mean Diffusivity (MD), derived from DTI and age. However, the fit of these linear regression models is unsatisfactory. Objectives: The purpose of our study was to investigate the age-related changes of WM in an elderly population. Methods: We performed statistical parametric mapping with DTI to evaluate the patterns of age-related microscopic WM changes with voxel-based analysis in the elderly population (>55 years old). A linear regression model was used to examine the associations between DTI parameters and age. Results: The fit of the linear regression model depicting the association between mean global FA, MD values (FA, MD derived from global WM), and age was better than that reported in prior studies using DTI (R2=0.4252, p

Purpose To solve the problem of the basal descent movement in quantification of the regional left ventricular (LV) myocardial wall thickness (WTh) and wall thickening (%WT) in short-axis (SA) cine MRI for effectively assessing the regional wall motion of LV myocardium. Materials and Methods LV long-axis tagged MRI and SA cine MRI were performed to calculate the longitudinal translation and circumferential WTh of LV myocardium in eight normal volunteers. The new SA end-systolic thickness (EST) data were reconstructed from the original EST data, based on the quantified longitudinal translation of LV myocardium. Results The mean %WT of six segments in the basal section after correction was significantly different from that before correction in both intra- and inter-operator experiments. The polar map also showed the significant improvement of the variability of regional %WT and lack of quantification of %WT in the most basal SA slices after correction. Conclusion The proposed technique demonstrated an important advantage to calculate the %WT in the most basal SA myocardial tissue, which was considered difficult to be achieved using cine MRI. J. Magn. Reson. Imaging 2011;33:464–473. © 2011 Wiley-Liss, Inc.

The problem of the basal descent through-plane movement in quantitative measurement of regional left ventricular (LV) function has been recognized for a long time. But, it is a lack of sophisticated analysis to overcome the limitation.