High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management最新文献

筛选

英文中文

A two-stage framework for DIC image denoising and Gabor based GLCM feature extraction for pre-cancer diagnosis 一种用于癌前诊断的DIC图像去噪和基于Gabor的GLCM特征提取两阶段框架

High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management Pub Date : 2018-06-04 DOI: 10.1117/12.2291538

S. Mukhopadhyay, S. Pratiher, S. Mukherjee, Debdeep Dasgupta, N. Ghosh, P. Panigrahi

{"title":"A two-stage framework for DIC image denoising and Gabor based GLCM feature extraction for pre-cancer diagnosis","authors":"S. Mukhopadhyay, S. Pratiher, S. Mukherjee, Debdeep Dasgupta, N. Ghosh, P. Panigrahi","doi":"10.1117/12.2291538","DOIUrl":"https://doi.org/10.1117/12.2291538","url":null,"abstract":"In this paper a novel two-stage adaptive framework for denoising of differential interference contrast (DIC) images followed by Gabor based gray-level co-occurrence matrix (GLCM) feature extraction methodology is proposed. The first stage consists of a hybrid cascade of anisotropic diffusion denoising (Perona–Malik diffusion) and unsharp masking (USM) based detail enhancement filter to remove noise from DIC images without losing significant morphological features of healthy and precancerous tissues while enlarging the image sharpness. The hybrid filter parameters are obtained by joint stochastic optimization of the image quality metrics. The estimated denoised image with the highest signal to noise ratio (SNR) from Stage I, is used for subsequent textural feature extraction. GLCM window considers neighborhood blocks with similar local statistics with well-preserved local structures between a pixel texture and its nearest neighbors. The efficacy of our denoised DIC imaging with Gabor based GLCM feature descriptors in analysis of healthy and precancerous tissues is experimentally validated for its competitive denoising performance and detail structure preservation of DIC images. The relative change in magnitude and phase information as manifested from Gabor filter coupled with GLCM based spatial statistical measures of tissues as cancer progress validates the adequacy of the proposed scheme for its early stage cancer detection ability in cervical tissues.","PeriodicalId":447752,"journal":{"name":"High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123695556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2