On Detection of Stenosis-Type Sections in Fallopian Tubal Models Using Support Vector Machines

In this paper, a support-vector-machine(SVM)- based method of detecting stenosis is presented for fallopian tubal models. It copes with stenosis detection as classification of data prepared from results of ultrasonic measurements conducted for tubal models. Under assumption that waves reflected at the second and third boundary surfaces of the models potentially include characteristics associated with blocked sections (i.e., stenosis), the method determines the time range having the reflected waves, by referring to maximal values on envelope curves of them The determined range is divided into regular short intervals, and the difference between maximum value and minimum value on envelope curves is calculated for each interval. The ten-dimensional data used to SVM learning and stenosis detection is prepared from the frequency distribution of the number of the short intervals versus difference values. Experimental results establish that the method can achieves favorable accuracies in checking occurrence of stenosis and in identifying tubal model types.