scholarly journals Atypia and Surface Structure of Superficial Neoplasms of the Colon Less Than 10 mm in Diameter

1996 ◽  
Vol 2 (3) ◽  
pp. 135-146
Author(s):  
Masatoshi Yasuda
Keyword(s):  
Image Analysis ◽  
Discriminant Analysis ◽  
Surface Structure ◽  
Marginal Zone ◽  
Group 14 ◽  
Nuclear Atypia ◽  
Macroscopic Type ◽  
Index Group ◽  
Severe Atypia ◽  
Superficial Type

Histological specimens of 30 distinct adenomas and 30 distinct carcinomas were studied by image analysis to quantify nuclear size and shape. These data were used to derive a discriminant equation, which permitted the lesions to be classified into two groups based on nuclear atypia. Next, 50 superficial type tumors of the colon (34 IIa lesions, 9 IIc + IIa lesions, and 7 IIc lesions) <10 mm in longest diameter were similarly analyzed. These lesions were classified into a high atypia index group (HAI group, 14 lesions) and a low atypia index group (LAI group, 36 lesions) by the above discriminatory equation. Differences between these two groups in surface structure, marginal zone properties, and macroscopic type, assessed using a dissecting microscope, were studied. A hyperplastic shape of the tubular orifice was seen at the marginal zone in 10 lesions (27.7%) in the LAI group and 11 lesions (78.5%) in the HAI group. This difference was significant. Surface structure and macroscopic type were not correlated with the degree of atypia. The tubular density in lesions showing a hyperplastic pitted pattern at their border was 77.40, significantly higher than that for lesions without such a pattern (73.12). The contribution of various variables to surface structure, marginal zone properties, and macroscopic type was studied by discriminant analysis. A high correlation was found between marginal zone properties and tubular density. Since lesions with high nuclear atypia tend to have high tubular density, marginal zone properties were secondarily correlated with the level of nuclear atypia. Observation of the marginal zone properties of lesions was thus suggested to be helpful in the diagnosis of lesions with severe atypia.

Automated image analysis of nuclear atypia in high-power field histopathological image

2015 ◽  
Vol 258 (3) ◽  
pp. 233-240 ◽  
Author(s):  
CHENG LU ◽  
MENGYAO JI ◽  
ZHEN MA ◽  
MRINAL MANDAL
Keyword(s):  
Image Analysis ◽  
High Power ◽  
Automated Image Analysis ◽  
High Power Field ◽  
Nuclear Atypia ◽  
Histopathological Image

Color Based Classification for Berries of Japanese Blue Honeysuckle

2011 ◽  
Vol 7 (4) ◽  
Author(s):  
Longsheng Fu ◽  
Hiroshi Okamoto ◽  
Takashi Kataoka ◽  
Youichi Shibata
Keyword(s):  
Image Analysis ◽  
Standard Deviation ◽  
Discriminant Analysis ◽  
Success Rates ◽  
Fresh Market ◽  
Unique Form ◽  
Lonicera Caerulea ◽  
Lighting Conditions ◽  
Visual Characteristics ◽  
Blue Honeysuckle

Japanese blue honeysuckle (Lonicera caerulea L. var. emphyllocalyx Nakai) is a unique form of edible honeysuckle that has exceptionally tasty berries. Visual characteristics of the berry such as the color of the skin and the presence of defects are the most decisive factors in determining its quality. An image analysis based methodology for classifying the berries under uncontrolled outside lighting conditions was developed. A color sheet with hue value around 29° was determined as the background to support the berries whose hue values were found near 212°. With the thresholding level computed by Otsu’s algorithm in the red channel, berries were segmented from the background successfully. Three parameters, average and standard deviation of hue component and average of saturation component, were chosen as the best descriptions for each berry according to the Fisher’s least significant differences test. Three canonical functions and corresponding group centroids of each function obtained by discriminant analysis were able to classify the berries aimed for fresh market, processing, and waste at success rates of 95.1%, 85.1%, and 94.3%, respectively.

scholarly journals Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas

2005 ◽  
Vol 27 (4) ◽  
pp. 237-244 ◽  
Author(s):  
Hae-Gil Hwang ◽  
Hyun-Ju Choi ◽  
Byeong-Il Lee ◽  
Hye-Kyoung Yoon ◽  
Sang-Hee Nam ◽  
...  
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Visual Analysis ◽  
Ductal Carcinoma ◽  
Texture Features ◽  
Cancer Tissue ◽  
Histological Sections ◽  
Haar Transform ◽  
Ductal Hyperplasia

Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (CA). To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant) analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each), respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function.

Surface structure of microporous membranes by computerized SEM image analysis applied to Anopore filters

1997 ◽  
Vol 137 (1-2) ◽  
pp. 89-97 ◽  
Author(s):  
A. Hernández ◽  
J.I. Calvo ◽  
P. Prádanos ◽  
L. Palacio ◽  
M.L. Rodríguez ◽  
...  
Keyword(s):  
Image Analysis ◽  
Surface Structure ◽  
Microporous Membranes ◽  
Sem Image ◽  
Sem Image Analysis
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