Classification and indexing of gene expression images

Karthik Jayaraman; Sethuraman Panchanathan; Sudhir Kumar

doi:10.1117/12.449777

Classification and indexing of gene expression images

Karthik Jayaraman, Sethuraman Panchanathan, Sudhir Kumar

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

In this paper, we present an approach for classification and indexing of embryonic gene expression pattern images using shape descriptors for retrieval of data in the biological domain. For this purpose, the image is first subjected to a registration process that involves edge fitting and size-standardization. It is followed by segmentation in order to delineate the expression pattern from the cellular background. The moment invariants for the segmented pattern are computed. Image dissimilarity between images is computed based on these moment invariants for each image pair. Area and Centroids of the segmented expression shapes are used to neutralize the invariant behavior of moment invariants during image retrieval. Details of the proposed approach along with analysis of a pilot dataset are presented in this paper.

Original language	English (US)
Pages (from-to)	471-481
Number of pages	11
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	4472
DOIs	https://doi.org/10.1117/12.449777
State	Published - Dec 1 2001
Event	Applications for Digital Image Processing XXIV - San Diego, CA, United States Duration: Jul 31 2001 → Aug 3 2001

Keywords

Classification
Content-based retrieval
Gene expression image database
Indexing
Pattern recognition
Shape features

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.449777

Cite this

@article{3017ad398127410d948dd35814567e1c,

title = "Classification and indexing of gene expression images",

abstract = "In this paper, we present an approach for classification and indexing of embryonic gene expression pattern images using shape descriptors for retrieval of data in the biological domain. For this purpose, the image is first subjected to a registration process that involves edge fitting and size-standardization. It is followed by segmentation in order to delineate the expression pattern from the cellular background. The moment invariants for the segmented pattern are computed. Image dissimilarity between images is computed based on these moment invariants for each image pair. Area and Centroids of the segmented expression shapes are used to neutralize the invariant behavior of moment invariants during image retrieval. Details of the proposed approach along with analysis of a pilot dataset are presented in this paper.",

keywords = "Classification, Content-based retrieval, Gene expression image database, Indexing, Pattern recognition, Shape features",

author = "Karthik Jayaraman and Sethuraman Panchanathan and Sudhir Kumar",

year = "2001",

month = dec,

day = "1",

doi = "10.1117/12.449777",

language = "English (US)",

volume = "4472",

pages = "471--481",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

note = "Applications for Digital Image Processing XXIV ; Conference date: 31-07-2001 Through 03-08-2001",

}

TY - JOUR

T1 - Classification and indexing of gene expression images

AU - Jayaraman, Karthik

AU - Panchanathan, Sethuraman

AU - Kumar, Sudhir

PY - 2001/12/1

Y1 - 2001/12/1

N2 - In this paper, we present an approach for classification and indexing of embryonic gene expression pattern images using shape descriptors for retrieval of data in the biological domain. For this purpose, the image is first subjected to a registration process that involves edge fitting and size-standardization. It is followed by segmentation in order to delineate the expression pattern from the cellular background. The moment invariants for the segmented pattern are computed. Image dissimilarity between images is computed based on these moment invariants for each image pair. Area and Centroids of the segmented expression shapes are used to neutralize the invariant behavior of moment invariants during image retrieval. Details of the proposed approach along with analysis of a pilot dataset are presented in this paper.

AB - In this paper, we present an approach for classification and indexing of embryonic gene expression pattern images using shape descriptors for retrieval of data in the biological domain. For this purpose, the image is first subjected to a registration process that involves edge fitting and size-standardization. It is followed by segmentation in order to delineate the expression pattern from the cellular background. The moment invariants for the segmented pattern are computed. Image dissimilarity between images is computed based on these moment invariants for each image pair. Area and Centroids of the segmented expression shapes are used to neutralize the invariant behavior of moment invariants during image retrieval. Details of the proposed approach along with analysis of a pilot dataset are presented in this paper.

KW - Classification

KW - Content-based retrieval

KW - Gene expression image database

KW - Indexing

KW - Pattern recognition

KW - Shape features

UR - http://www.scopus.com/inward/record.url?scp=0035765568&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035765568&partnerID=8YFLogxK

U2 - 10.1117/12.449777

DO - 10.1117/12.449777

M3 - Conference article

AN - SCOPUS:0035765568

SN - 0277-786X

VL - 4472

SP - 471

EP - 481

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Applications for Digital Image Processing XXIV

Y2 - 31 July 2001 through 3 August 2001

ER -

Classification and indexing of gene expression images

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this