In stomach tumor imaging, traditional time domain algorithm, i.e., back projection (BP) algorithm, and traditional frequency domain algorithm, i.e., frequency wave number migration (F-K) algorithm, can locate tumor target accurately. However, BP and F-K algorithms perform poorly in identifying tumor sizes and shapes. The algorithms must consider the influence of various tissues in the human body: the attenuation of the signal strength of electromagnetic waves, the decrease in speed and the refraction due to the different permittivities between different organs of the body. These factors will eventually lead to image offset and even generate a virtual image. It is effective to refrain the displacement of image with modifying the time element of the imaging algorithm by iteration. This paper proposes a method based on combination of support vector machine (SVM) with BP and F-K algorithms to solve problems in recognizing tumor shape. The method uses field strength obtained by BP and F-K algorithms as input in SVM to establish the SVM model. Based on BP algorithm, recognition method for SVM includes the following characteristics: short prediction time of SVM and good virtual elimination effect. However, the algorithm requires long periods and possibly misses tumor targets. Except the same characteristics as BP algorithm: short prediction time of SVM and good virtual elimination effect, F-K algorithm also works more efficiently, does not miss any tumor targets, and conforms more with requirements of real-time imaging. When the data are contaminated by noises, the tumor shape in the stomach can still be suitably predicted, which demonstrates the robustness of the method.
"Target Recognition Method for Support Vector Machine on Stomach Tumor Imaging," Progress In Electromagnetics Research B,
Vol. 78, 15-30, 2017. doi:10.2528/PIERB17050901
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