A. S. Jadhav, S. Banerjee, P. Dutta, R. Paul, M. Pal, P. Banerjee, K. Chaudhuri, J. Chatterjee
{"title":"Quantitative Analysis of Histopathological Features of Precancerous Lesion and Condition Using Image Processing Technique","authors":"A. S. Jadhav, S. Banerjee, P. Dutta, R. Paul, M. Pal, P. Banerjee, K. Chaudhuri, J. Chatterjee","doi":"10.1109/CBMS.2006.137","DOIUrl":null,"url":null,"abstract":"This paper aims at quantitative analysis of histopathological features of precancerous lesion and condition using image processing technique. The algorithm involves median and low pass filtering, segmentation by adaptive region growing, optimal and local thresholding, morphological operations such as opening and closing of gray scale and binary images and some numerical methods. Differentiation on the basis of type and level of precancerous type or condition is carried out based on image marker, defined as a vector of cancer related features viz. length and curvature of radius of rete-ridges and papillae, population density of cells within epithelium, etc. Implementation of presented algorithms is done in MATLAB. The results support quantitative analysis of pathological condition in respect with progression towards malignancy. This analysis may help in developing automated analysis tool","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2006.137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 31
Abstract
This paper aims at quantitative analysis of histopathological features of precancerous lesion and condition using image processing technique. The algorithm involves median and low pass filtering, segmentation by adaptive region growing, optimal and local thresholding, morphological operations such as opening and closing of gray scale and binary images and some numerical methods. Differentiation on the basis of type and level of precancerous type or condition is carried out based on image marker, defined as a vector of cancer related features viz. length and curvature of radius of rete-ridges and papillae, population density of cells within epithelium, etc. Implementation of presented algorithms is done in MATLAB. The results support quantitative analysis of pathological condition in respect with progression towards malignancy. This analysis may help in developing automated analysis tool