Deakin University
Browse

File(s) under permanent embargo

A noisy-smoothing relevance feedback method for content-based medical image retrieval

Version 2 2024-06-06, 10:10
Version 1 2015-03-30, 11:14
journal contribution
posted on 2024-06-06, 10:10 authored by Y Huang, H Huang, J Zhang
In this paper, we address a new problem of noisy images which present in the procedure of relevance feedback for medical image retrieval. We concentrate on the noisy images, caused by the users mislabeling some irrelevant images as relevant ones, and a noisy-smoothing relevance feedback (NS-RF) method is proposed. In NS-RF, a two-step strategy is proposed to handle the noisy images. In step 1, a noisy elimination algorithm is adopted to identify and eliminate the noisy images. In step 2, to further alleviate the influence of noisy images, a fuzzy membership function is employed to estimate the relevance probabilities of retained relevant images. After noisy handling, the fuzzy support vector machine, which can take into account different relevant images with different relevance probabilities, is adopted to re-rank the images. The experimental results on the IRMA medical image collection demonstrate that the proposed method can deal with the noisy images effectively.

History

Journal

Multimedia Tools and Applications

Volume

73

Pagination

1963-1981

Location

New York, NY

ISSN

1380-7501

eISSN

1573-7721

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2014, Springer

Issue

3

Publisher

Springer