Wavelet features have a high potential for effective differentiation of benign from malignant thyroid nodules on sonography.

Texture analysis is a reliable method and has the potential to be used effectively for classification of benign and malignant tumors on breast sonography.

An in vivo micronucleus assay using mouse bone marrow for identifying the radioprotective effect of cimetidine is described. The influence of cimetidine, an antagonist to the histamine H2 receptor, on the kinetics of radiation-induced micronuclei was tested in the CD-1 male mouse. Cimetidine was administered at 15 mg/kg i.p. 2 h prior to irradiation of mouse given various doses of gamma-rays from 0.25 to 1 Gy. The frequency of micronucleated polychromatic erythrocytes (PCEs) and normochromatic erythrocytes (NCEs) per 1500 PCEs were determined at 36, 48 and 72 h post-irradiation. The results obtained indicate a linear dose response for three sampling times, and that cimetidine reduces the number of micronuclei in both PCE and NCE at all sampling times, as well as reducing radiation cytotoxicity. When the overall effects of radiation alone or in the presence of cimetidine are compared, a dose reduction factor (DRF) of 1.5 was found for cimetidine in the dose range used in this study, which is statistically highly significant (p < 0.001). This DRF at the low dosage of cimetidine used in this study compared with known radioprotectors is very promising and it might be useful as a potent radioprotector. The mechanism by which cimetidine reduces clastogenic effects of radiation is not well understood. We propose that it might act by a radical scavenging mechanism via enzyme catalysis.

Introduction:Visual inspection by magnetic resonance (MR) images cannot detect microscopic tissue changes occurring in MS in normal appearing white matter (NAWM) and may be perceived by the human eye as having the same texture as normal white matter (NWM). The aim of the study was to evaluate computer aided diagnosis (CAD) system using texture analysis (TA) in MR images to improve accuracy in identification of subtle differences in brain tissue structure.Material and Methods:The MR image database comprised 50 MS patients and 50 healthy subjects. Up to 270 statistical texture features extract as descriptors for each region of interest. The feature reduction methods used were the Fisher method, the lowest probability of classification error and average correlation coefficients (POE+ACC) method and the fusion Fisher plus the POE+ACC (FFPA) to select the best, most effective features to differentiate between MS lesions, NWM and NAWM. The features parameters were used for texture analysis with principle component analysis (PCA) and linear discriminant analysis (LDA). Then first nearest-neighbour (1-NN) classifier was used for features resulting from PCA and LDA. Receiver operating characteristic (ROC) curve analysis was used to examine the performance of TA methods.Results:The highest performance for discrimination between MS lesions, NAWM and NWM was recorded for FFPA feature parameters using LDA; this method showed 100% sensitivity, specificity and accuracy and an area of Az = 1 under the ROC curve.Conclusion:TA is a reliable method with the potential for effective use in MR imaging for the diagnosis and prediction of MS.