The purpose of this work is to develop a new pattern recognition method using the higher-order autocorrelation features (HOAFs), and to apply this to our microcalcification detection system on mammographic images. Microcalcification is a typical sign of breast cancer and tends to show up as very subtle shadows. We developed a triple-ring filter for detecting microcalcifications, and the prototype detection system is nearly complete. However, our prototype system does not allow for the detection of three types of microcalcifications, two of which are amorphous and linear microcalcifications and the third is obscured microcalcifications which is often confused with the background or circumference that have almost the same density. We targeted the amorphous type of microcalcification, which has a low contrast and easily goes undetected. The various features of microcalcifications and false-positive (FP) shadows were extracted and trained using the multi-regression analysis, and unknown images were recognized as a result of this training. As a result, amorphous microcalcifications were successfully detected with no increase in the number of FPs compared with our existing detection method.
A method for measuring the characteristic curves generated by the mammography imaging systems has not yet
been well established due to poor quality control over X-ray exposure in the range of kV values, which is lower than the
conventional quality. In this paper, we proposed a bootstrap method using a “stepwedge” designed for characteristic
curve measurement in mammography. A ten-step stepwedge containing calcium phosphate, with each step having a
different density of material, was employed. In our experiment, the tube voltage and mA values were changed in the
range of 25 to 32 kV at increments of 1 kV and in the range of 20 to 100 mAs at increments of 20 mAs, respectively.
The results of the curve measurements indicated that our method might be useful to both screen-film mammography and
computed radiography (CR), although additional experiments to evaluate the accuracy and precision of the acquired
data are required.