Lesion detection using Gabor-based saliency field mapping

Abstract

In this paper, we present a method that detects lesions in two-dimensional (2D) cross-sectional brain images. By calculating the major and minor axes of the brain, we calculate an estimate of the background, without any a priori information, to use in inverse filtering. Shape saliency computed by a Gabor filter bank is used to further refine the results of the inverse filtering. The proposed algorithm was tested on different images of "The Whole Brain Atlas" database. 8 The experimental results have produced 93% classification accuracy in processing 100 arbitrary images, representing different kinds of brain lesion.

Non-linear IR Scene Prediction for Range Video Surveillance

“…Figure 3 shows a sample set of images from the selected database, which involves the motion of a person from left to right within the screen window. We have adopted the same formulation for the Gabor filter as Macenko [20], which specifies the Gabor filter variables to be Eight different orientations for the Gabor bank are adapted since more would not provide any significant improvement and fewer would likely not discern enough about the image. Upon passing the image through the filter bank a combined saliency image is created.…”

“…Montillo et al's work [19] is based on the Gabor filter bank adjusted to the filter's orientation and the radial frequency and angle of the filter's sinusoidal grating to extract information about the deformation of tissue. Another example is Macenko et al's work [20], which provides both a good explanation of the approach to using Gabor filtering and a highly relevant practical application in lesion detection within the brain. A slightly older work by Lang and Yishman [21] describes the process of using Gabor filtering to help detect changes in terrain.…”

Multiple Vehicle Tracking Using Gabor Filter Bank Predictor

“…Figure 2 shows two example images from the selected databases depicting the scenes from which they were acquired. In the implementation, we follow the same discrete formulation of the Gabor filter as in Macenko et. al (2007), which specifies the Gabor filter variables to be S x = 1 , S y = 1 , and…”