Classification of gastric tumors using shape features of gland

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.