TY - JOUR
T1 - Classification of gastric tumors using shape features of gland
AU - Tanaka, Toshiyuki
AU - Uchino, Yoshitaka
AU - Oka, Teruaki
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - Recently in Japan, pathologists have been in short supply, while each pathological diagnosis requires a substantial amount of time because each analyte must be inspected by multiple pathologists for adequate diagnosis. This paper deals with the classification method of gastric cancer and gastric adenoma, using image processing and pattern analysis. We first select the R component and G component from the RGB basis of the digital image, and the Y component from the YIQ basis for our system. After pre-processing, we automatically extracted the shape of the nucleus and cytoplasm. After many inspections, we selected 40 features for shape of the nucleus and cytoplasm and 14 features for texture within the cytoplasm for assessment of tumors. Principal component analysis, F test of homoscedasticity. t test of difference of average, stepwise method for selecting the smaller number of features, and discriminant method using Mahalanobis distance were all performed. Total ratio of diagnosis reached 96.9%. showing the validity of our proposed method.
AB - Recently in Japan, pathologists have been in short supply, while each pathological diagnosis requires a substantial amount of time because each analyte must be inspected by multiple pathologists for adequate diagnosis. This paper deals with the classification method of gastric cancer and gastric adenoma, using image processing and pattern analysis. We first select the R component and G component from the RGB basis of the digital image, and the Y component from the YIQ basis for our system. After pre-processing, we automatically extracted the shape of the nucleus and cytoplasm. After many inspections, we selected 40 features for shape of the nucleus and cytoplasm and 14 features for texture within the cytoplasm for assessment of tumors. Principal component analysis, F test of homoscedasticity. t test of difference of average, stepwise method for selecting the smaller number of features, and discriminant method using Mahalanobis distance were all performed. Total ratio of diagnosis reached 96.9%. showing the validity of our proposed method.
KW - Classification
KW - Computer diagnosis
KW - Gastric tumor
KW - Gland
KW - Shape feature
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U2 - 10.1541/ieejeiss.126.1242
DO - 10.1541/ieejeiss.126.1242
M3 - Article
AN - SCOPUS:33750310907
VL - 126
SP - 1242-1248+7
JO - IEEJ Transactions on Electronics, Information and Systems
JF - IEEJ Transactions on Electronics, Information and Systems
SN - 0385-4221
IS - 10
ER -