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SSG Seminar Abstract

Erik Learned-Miller
CS, Univ. of Massachusetts-Amherst

Joint Bias Removal in MRI Using Entropy Minimization

The correction of multiplicative bias in magnetic resonance images is an important problem in medical image processing, especially as a preprocessing step for quantitative measurements and other numerical procedures. Most previous approaches have used a maximum likelihood method to increase the probability of the pixels in a single image by adaptively estimating a correction to the unknown image bias field. The pixel probabilities are defined either in terms of a pre-existing tissue model, or nonparametrically in terms of the imageís own pixel values. In both cases, the specific location of a pixel in the image does not influence the probability calculation. Our approach, similar to methods of joint registration, simultaneously eliminates the bias from a set of images of the same anatomy, but from different patients. We use the statistics from the same location across different patientsí images, rather than within an image, to eliminate bias fields from all of the images simultaneously. Evaluating the likelihood of a particular voxel in one patientís scan with respect to voxels in the same location in a set of other patientsí scans disambiguates effects that might be due to either bias fields or anatomy.

Building a Classification Cascade for Visual Identification from One Example

Object identification (OID) is specialized recognition where the category is known (e.g.~cars) and the algorithm recognizes an object's exact identity (e.g.~Bob's BMW). Two special challenges characterize OID. (1) Inter-class variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. (2) There may be many classes but few or just one positive ``training'' examples per class. Due to (1), a solution must locate possibly subtle object-specific salient features (a door handle) while avoiding distracting ones (e.g.~specular highlights). However, (2) rules out direct techniques of feature selection. We describe an on-line algorithm that takes one query image from a known category and builds an efficient ``same'' vs.~``different'' classification cascade by predicting the most discriminative feature set for that object. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered feature sequence unique to a specific query image, maximizing cumulative information content.

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