scholarly journals Effect of Quantitative Nuclear Image Features on Recurrence of Ductal Carcinoma In Situ (DCIS) of the Breast

2008 ◽  
Vol 6 ◽  
pp. CIN.S401 ◽  
Author(s):  
David E. Axelrod ◽  
Naomi A. Miller ◽  
H. Lavina Lickley ◽  
Jin Qian ◽  
William A. Christens-Barry ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Image Features ◽  
Nuclear Grade ◽  
Image Feature ◽  
Group A ◽  
Group B ◽  
Nuclear Features

Background Nuclear grade has been associated with breast DCIS recurrence and progression to invasive carcinoma; however, our previous study of a cohort of patients with breast DCIS did not find such an association with outcome. Fifty percent of patients had heterogeneous DCIS with more than one nuclear grade. The aim of the current study was to investigate the effect of quantitative nuclear features assessed with digital image analysis on ipsilateral DCIS recurrence. Methods Hematoxylin and eosin stained slides for a cohort of 80 patients with primary breast DCIS were reviewed and two fields with representative grade (or grades) were identified by a Pathologist and simultaneously used for acquisition of digital images for each field. Van Nuys worst nuclear grade was assigned, as was predominant grade, and heterogeneous grading when present. Patients were grouped by heterogeneity of their nuclear grade: Group A: nuclear grade 1 only, nuclear grades 1 and 2, or nuclear grade 2 only (32 patients), Group B: nuclear grades 1, 2 and 3, or nuclear grades 2 and 3 (31 patients), Group 3: nuclear grade 3 only (17 patients). Nuclear fine structure was assessed by software which captured thirty-nine nuclear feature values describing nuclear morphometry, densitometry, and texture. Step-wise forward Cox regressions were performed with previous clinical and pathologic factors, and the new image analysis features. Results Duplicate measurements were similar for 89.7% to 97.4% of assessed image features. The rate of correct classification of nuclear grading with digital image analysis features was similar in the two fields, and pooled assessment across both fields. In the pooled assessment, a discriminant function with one nuclear morphometric and one texture feature was significantly (p = 0.001) associated with nuclear grading, and provided correct jackknifed classification of a patient's nuclear grade for Group A (78.1%), Group B (48.4%), and Group C (70.6%). The factors significantly associated with DCIS recurrence were those previously found, type of initial presentation (p = 0.03) and amount of parenchymal involvement (p = 0.05), along with the morphometry image feature of ellipticity (p = 0.04). Conclusion Analysis of nuclear features measured by image cytometry may contribute to the classification and prognosis of breast DCIS patients with more than one nuclear grade.

Classification of Oryza Sativa using Digital Image Analysis

2019 ◽  
Author(s):  
Davood Pour Yousefian Barfeh ◽  
Patrice Xandria Mari Delos Reyes ◽  
Myrna Coliat ◽  
Favis Joseph Balinado ◽  
Jessie Montalbo ◽  
...  
Keyword(s):  
Image Analysis ◽  
Oryza Sativa ◽  
Digital Image ◽  
Digital Image Analysis

scholarly journals Heterogeneity between Ducts of the Same Nuclear Grade Involved by Duct Carcinoma In Situ (DCIS) of the Breast

2010 ◽  
Vol 9 ◽  
pp. CIN.S5505 ◽  
Author(s):  
Naomi A. Miller ◽  
Judith-Anne W. Chapman ◽  
Jin Qian ◽  
William A. Christens-Barry ◽  
Yuejiao Fu ◽  
...  
Keyword(s):  
Digital Image ◽  
Breast Biopsy ◽  
Image Cytometry ◽  
Image Features ◽  
Nuclear Grade ◽  
Management Decision ◽  
Duct Carcinoma ◽  
Quantitative Image ◽  
Management Decision Making ◽  
Nuclear Features

Purpose Nuclear grade of breast DCIS is considered during patient management decision-making although it may have only a modest prognostic association with therapeutic outcome. We hypothesized that visual inspection may miss substantive differences in nuclei classified as having the same nuclear grade. To test this hypothesis, we measured subvisual nuclear features by quantitative image cytometry for nuclei with the same grade, and tested for statistical differences in these features. Experimental design and statistical analysis Thirty-nine nuclear digital image features of about 100 nuclei were measured in digital images of H<E stained slides of 81 breast biopsy specimens. One field with at least 5 ducts was evaluated for each patient. We compared features of nuclei with the same grade in multiple ducts of the same patient with ANOVA (or Welch test), and compared features of nuclei with the same grade in two ducts of different patients using 2-sided t-tests ( P ≤ 0.05). Also, we compared image features for nuclei in patients with single grade to those with the same grade in patients with multiple grades using t-tests. Results Statistically significant differences were detected in nuclear features between ducts with the same nuclear grade, both in different ducts of the same patient, and between ducts in different patients with DCIS of more than one grade. Conclusion Nuclei in ducts visually described as having the same nuclear grade had significantly different subvisual digital image features. These subvisual differences may be considered additional manifestations of heterogeneity over and above differences that can be observed microscopically. This heterogeneity may explain the inconsistency of nuclear grading as a prognostic factor.

Digital Image Analysis for Software Separation and Classification of Touching Grains: II. Classification

1995 ◽  
Vol 38 (2) ◽  
pp. 645-649 ◽  
Author(s):  
P. Shatadal ◽  
D.S. Jayas ◽  
N.R. Bulley
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis

Tu2048 Histological Features Using Quantitative Morphometric Digital Image Analysis Can Predict Hpvg and Help in Sub-Classification of Cirrhosis

2013 ◽  
Vol 144 (5) ◽  
pp. S-912
Author(s):  
Ahmad A. Alduaij ◽  
Dhanpat Jain ◽  
Andrew Lesniak ◽  
Anthony J. Demetris ◽  
Guadalupe Garcia-Tsao ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Histological Features

Evaluation of the metrological characteristics of a scanning flour analyzer in determining the optical properties of components by digital image analysis

2021 ◽  
Vol 30 (7) ◽  
pp. 36-38
Author(s):  
T.S. Shteinberg ◽  
◽  
O.V. Morozova ◽  
Keyword(s):  
Image Analysis ◽  
Optical Properties ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Metrological Characteristics ◽  
Image Method ◽  
Experimental Sample ◽  
Traceability Chain ◽  
Group A

The results of evaluating the metrological characteristics of an experimental sample of a scanning flour analyzer when determining the optical properties of components along the traceability chain of the production of group A pasta by the digital image method with alternate replacement of standard flatbed scanners (imitation of several copies of a scanning analyzer in different laboratories) are presented.

scholarly journals Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

2010 ◽  
Vol 30 (4) ◽  
pp. 340-344 ◽  
Author(s):  
Lucas B. Moraes ◽  
Fernando S. Osório ◽  
Felipe O. Salle ◽  
Guilherme F. Souza ◽  
Hamilton L.S. Moraes ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Digital Data ◽  
Bursa Of Fabricius ◽  
Diagnosis And Classification ◽  
Lymphoid Depletion ◽  
Alternative Methodology ◽  
Artificial Neuronal Network

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Digital Image Analysis for Software Separation and Classification of Touching Grains: I. Disconnect Algorithm

1995 ◽  
Vol 38 (2) ◽  
pp. 635-643 ◽  
Author(s):  
P. Shatadal ◽  
D. S. Jayas ◽  
N. R. Bulley
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis
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