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 F...

Full description

Saved in:

Bibliographic Details
Published in:	2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) pp. 870 - 875
Main Authors:	Orozco, H. M., Villegas, O. O. V., Maynez, L. O., Sanchez, V. G. C., de Jesus Ochoa Dominguez, H.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-07-2012
Subjects:	Cancer Computed tomography Computer Tomography (CT) Discrete Cosine Transform (DCT) Discrete cosine transforms Fast Fourier Transform (FFT) Feature extraction Frequency domain analysis Lung nodule detection (LND) Lungs Support Vector Machine (SVM) Support vector machines
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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%.
ISBN:	9781467303811 146730381X
DOI:	10.1109/ISSPA.2012.6310676