We developed and performance-tested a portable graphite calorimeter designed to measure the absolute dosimetry of various beams including heavy-ion beams, based on a flexible and convenient means of measurement. This measurement system is fully remote-controlled by the GPIB system. This system uses a digital PID (Proportional, Integral, Derivative) control method based on the Lab VIEW software. It was possible to attain stable conditions in a shorter time by this system. The standard deviation of the measurements using the calorimeter was 0.79% at a dose rate of 0.8 Gy/min in 17 calorimeter runs for a ⁶⁰Co photon beam. The overall uncertainties for the absorbed dose to graphite and water of the ⁶⁰Co photon beam using the developed calorimeter were 0.89% and 1.35%, respectively. Estimations of the correction factors due to vacuum gaps, impurities in the core, the dose gradient and the radiation profile were included in the uncertainties. The absorbed doses to graphite and water irradiated by the ⁶⁰Co photon beam were compared with dosimetry measurements obtained using three ionization chambers. The absorbed doses to graphite and water estimated by the two dosimetry methods agreed within 0.1% and 0.3%, respectively.

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We previouslyi ntroduced a quasi-fract a l dimension (Q-FD) to enhance breast cancer detection in X-ray mammography. In the present study, we evaluated the usefulness of this image feature for differentiating between benign and malignant masses using a support vector machine (SVM) with various kernels. The kernel computes the inner product of the functions that embed the data into a feature space where the nonlinear pattern appears linear. Q-FD was calculated using the method previously reported from the database of X-ray mammograms produced by the Japan Society of Radiological Technology. In addition to Q-FD, the image features such as curvature (C) and eccentricity (E) were extracted. The conventional fractal dimension (C-FD) was also calculated using the box-counting method.
First, we investigatedt he SVM performance in terms of accuracy, s ensitivity and specificity in the task of differentiating between benign and malignant masses by taking 5 parameters (C, E, C-FD, Q-FD and age) as input features in SVM. When using the linear kernel, the best accuracy was obtained at a regularization Orameter of 50. For the polynomial and radial basis function (RBF) kernels, the best accuracy was obtained when the degree of polynomial and the width of RBF were 1 and 1, respectively. The accuracies were 0.746±0.089,0.731±0.095 and 0.734±0.086 for the linear, polynomial and RBF kernels, respectively, when using C, E, C- FD and age as input features in the SVM. When Q-FD was added to the above input features, the accuracies were significantly improved to 0.957±0.045,0.950±0.045 and 0.949±0.052 for the linear, polynomial and RBF kernels, respectively. These results suggest that Q-FD is effective for discriminating between benign and malignant masses and SVM is highly recommended as a classifier for its simple utilization and good performance, especially when the training set size is small.

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