Three dimensional texture analysis of multidetector computed tomography images of the liver

Balaji Ganeshan; Rupert Young; Kenneth Miles; Chris Chatwin

doi:10.1117/12.664027

17 April 2006 Three dimensional texture analysis of multidetector computed tomography images of the liver

Balaji Ganeshan, Rupert Young, Kenneth Miles, Chris Chatwin

Proceedings Volume 6245, Optical Pattern Recognition XVII; 62450K (2006) https://doi.org/10.1117/12.664027
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

A three dimensional (3-D) Multidetector Computed Tomography (MDCT) liver volume dataset of a patient with colorectal cancer was employed, each slice containing 512 x 512 pixels with pixel dimensions of 0.84 mm x 0.84 mm and slice reconstruction at a 2 mm interval. The 2-D slices were down-sampled to obtain a pixel width of 2mm x 2mm, thus resulting in an isometric cubic voxel of dimension of 2mm³. A 3-D Laplacian of Gaussian filter mask was generated for different pass-band regions in order to highlight fine to coarse texture within the volume. 3-D Fourier transformations of the liver volume and filter mask were obtained and their product computed. The filtered volume in the spatial domain was obtained by taking the inverse 3-D Fourier transform of this product. This 3D filtered volume highlighted textural features within different bands of spatial frequencies. The 3-D texture of the liver was viewed using maximal intensity projection. We quantify the texture over different spatial scale ranges for 3-D filtered liver volume which may have the potential of improving our preliminary results of 2-D texture analysis of liver images of patients with colorectal cancer and therefore could be employed as a computer assisted diagnostic tool in medical cancer imaging.

Citation Download Citation

Balaji Ganeshan, Rupert Young, Kenneth Miles, and Chris Chatwin "Three dimensional texture analysis of multidetector computed tomography images of the liver", Proc. SPIE 6245, Optical Pattern Recognition XVII, 62450K (17 April 2006); https://doi.org/10.1117/12.664027

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KEYWORDS

Liver

3D image processing

Electronic filtering

Colorectal cancer

Image filtering

Fourier transforms

Gaussian filters

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