Skip navigation
Skip navigation

Fisher tensors for classifying human epithelial cells

Faraki, Masoud; Harandi, Mehrtash; Wiliem, Arnold; Lovell, Brian C

Description

Analyzing and classifying Human Epithelial type 2 (HEp-2) cells using Indirect Immunofluorescence protocol has been the golden standard for detecting connective tissue diseases such as Rheumatoid Arthritis. However, this suffers from numerous shortcomings such as being subjective as well as time and labor intensive. Recently, several studies explore the advantages of artificial systems to automate the process, not only to reduce the test turn-around time but also to deliver more consistent...[Show more]

dc.contributor.authorFaraki, Masoud
dc.contributor.authorHarandi, Mehrtash
dc.contributor.authorWiliem, Arnold
dc.contributor.authorLovell, Brian C
dc.date.accessioned2015-12-13T22:17:38Z
dc.identifier.issn0031-3203
dc.identifier.urihttp://hdl.handle.net/1885/71247
dc.description.abstractAnalyzing and classifying Human Epithelial type 2 (HEp-2) cells using Indirect Immunofluorescence protocol has been the golden standard for detecting connective tissue diseases such as Rheumatoid Arthritis. However, this suffers from numerous shortcomings such as being subjective as well as time and labor intensive. Recently, several studies explore the advantages of artificial systems to automate the process, not only to reduce the test turn-around time but also to deliver more consistent results. In this paper, we extend the conventional bag of word models from Euclidean space to non-Euclidean Riemannian manifolds and utilize them to classify the HEp-2 cells. The main motivation comes from the observation that HEp-2 cells can be efficiently described by symmetric positive definite matrices which lie on a Riemannian manifold. With this motivation, we first discuss an intrinsic bag of Riemannian words model. We then propose Fisher tensors which can in turn encode additional information about the distribution of the signatures in a bag of word model. Experiments on two challenging HEp-2 images datasets, namely ICPRContest and SNPHEp-2 show that the proposed methods obtain notable improvements in discrimination accuracy, in comparison to baseline and several state-of-the-art methods. The proposed framework, while hand-crafted towards cell classification, is a generic framework for object recognition. This is supported by assessing the performance of our proposal on a challenging texture classification task.
dc.publisherPergamon-Elsevier Ltd
dc.sourcePattern Recognition
dc.titleFisher tensors for classifying human epithelial cells
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume47
dc.date.issued2014
local.identifier.absfor089900 - OTHER INFORMATION AND COMPUTING SCIENCES
local.identifier.ariespublicationU3488905xPUB2627
local.type.statusPublished Version
local.contributor.affiliationFaraki, Masoud, The University of Queensland
local.contributor.affiliationHarandi, Mehrtash, College of Engineering and Computer Science, ANU
local.contributor.affiliationWiliem, Arnold, The University of Queensland
local.contributor.affiliationLovell, Brian C, University of Queensland
local.description.embargo2037-12-31
local.bibliographicCitation.issue7
local.bibliographicCitation.startpage2348
local.bibliographicCitation.lastpage2359
local.identifier.doi10.1016/j.patcog.2013.10.011
dc.date.updated2015-12-11T07:35:38Z
local.identifier.scopusID2-s2.0-84897110128
local.identifier.thomsonID000334978100006
CollectionsANU Research Publications

Download

File Description SizeFormat Image
01_Faraki_Fisher_tensors_for_classifying_2014.pdf2.11 MBAdobe PDF    Request a copy


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator