METHODS: The European Association of Nuclear Medicine (EANM) procedure guidelines version 2.0 for FDG-PET tumor imaging has adhered for this purpose. A NEMA2012/IEC2008 phantom was filled with tumor to background ratio of 10:1 with the activity concentration of 30 kBq/ml ± 10 and 3 kBq/ml ± 10% for each radioisotope. The phantom was scanned using different acquisition times per bed position (1, 5, 7, 10 and 15 min) to determine the Tmin. The definition of Tmin was performed using an image coefficient of variations (COV) of 15%.
RESULTS: Tmin obtained for 18F, 68Ga and 124I were 3.08, 3.24 and 32.93 min, respectively. Quantitative analyses among 18F, 68Ga and 124I images were performed. Signal-to-noise ratio (SNR), contrast recovery coefficients (CRC), and visibility (VH) are the image quality parameters analysed in this study. Generally, 68Ga and 18F gave better image quality as compared to 124I for all the parameters studied.
CONCLUSION: We have defined Tmin for 18F, 68Ga and 124I SPECT CT imaging based on NEMA2012/IEC2008 phantom imaging. Despite the long scanning time suggested by Tmin, improvement in the image quality is acquired especially for 124I. In clinical practice, the long acquisition time, nevertheless, may cause patient discomfort and motion artifact.
MATERIALS AND METHODS: Two hundred retinal samples of right eye [57.0% females (n = 114) and 43.0% males (n = 86)] were selected from baseline visit. A custom-written software was used for vessel segmentation. Vessel segmentation is the process of transforming two-dimensional color images into binary images (i.e. black and white pixels). The circular area of approximately 2.6 optic disc radii surrounding the center of optic disc was cropped. The non-vessels fragments were removed. FracLac was used to measure the fractal dimension and vessel density of retinal vessels.
RESULTS: This study suggested that 14.1% of the region of interest (i.e. approximately 2.6 optic disk radii) comprised retinal vessel structure. Using correlation analysis, vessel density measurement and fractal dimension estimation are linearly and strongly correlated (R = 0.942, R(2) = 0.89, p
METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.
RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.
CONCLUSIONS: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.