Work

Microscopic image classification using sparsity in a transform domain and Bayesian learning

Year: 2011

Type: conference-paper

Abstract: Some biomedical images show a large quantity of different junctions and sharp corners. It is possible to classify several types of biomedical images in a region covariance approach. Cancer cell line images are divided into small blocks and covariance matrices of image blocks are computed. Eigen-values of the covariance matrices are used as classification parameters in a Bayesian framework using the sparsity of the parameters in a transform domain. The efficiency of the proposed method over classification using standard Support Vector Machines (SVM) is demonstrated on biomedical image data. © 2011 EURASIP.

Source: Bilkent University Institutional Repository (Bilkent University)

Authors: Alexander Suhre, Tülin Erşahin, Rengül Cetin‐Atalay, A. Enis Çetin

Institution: Bilkent University

Language: English

Topic: Image Retrieval and Classification Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: Microscopic Image Processing, Analysis, Classification and Modelling Environment

OpenAlex ID: W2139879154

FWCI: 0.475

Cites: 13

Cited by: 2

Related to: 10