Medical volume segmentation using bank of Gabor filters

Authors:
Adebayo Olowoyeye;Mihran Tuceryan;Shiaofen Fang
Affiliations:
Indiana University, Bloomington, IN;IUPUI, Indianapolis, IN;IUPUI, Indianapolis, IN
Venue:
Proceedings of the 2009 ACM symposium on Applied Computing
Year:
2009

Citing 3
Cited 0

Multichannel Texture Analysis Using Localized Spatial Filters

IEEE Transactions on Pattern Analysis and Machine Intelligence
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead

IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Segmentation of a 3D Seismic Section with Wavelet Transform and Gabor Filters

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we will present an unsupervised approach for segmenting medical volume images based on texture properties. The texture properties of the volume data are defined based on spatial frequencies as implemented using a statistical method known as Gabor filters. Each Gabor filter in the bank is tuned to detect patterns of a specific frequency and orientation when convolved with a medical volume. The convolution is performed in the Fourier domain and the resulting response image is a feature which is added to our feature vector. The feature vector is thus passed into a classification/segmentation algorithm.