Lung nodule classification in frequency domain using support vector machines

In this paper a computational alternative to classify lung nodules inside CT thorax images in the frequency domain is presented. After image acquisition, a region of interest is manually selected. Then, the spectrums of the two dimensional Discrete Cosine Transform (2D-DCT) and the two dimensional Fast Fourier Transform (2D-FFT) were calculated. Later, two statistical texture features were extracted from the histogram computed from the spectrum of each CT image. Finally, a support vector machine with a radial basis function as a kernel was used as the classifier. Seventy five tests with different diagnosis and number of images were used to validate the methodology presented. After experimentation and results, ten false negatives (FN) and two false positives (FP) were obtained, and a sensitivity and specificity of 96.15% and 52.17% respectively. The total preciseness obtained with the methodology proposed was 82.66%.