Differentiation of benign from malignant nonpalpable breast masses: A comparison of computer-assisted quantification and visual assessment of lesion stiffness with the use of sonographic elastography

2010 ◽  
Vol 51 (1) ◽  
pp. 9-14 ◽  
Author(s):  
Se Yeong Chung ◽  
Woo Kyung Moon ◽  
Ji Won Choi ◽  
Nariya Cho ◽  
Mijung Jang ◽  
...  
Keyword(s):  
Visual Assessment ◽  
Computer Assisted ◽  
Breast Masses ◽  
Sonographic Elastography

scholarly journals Visual assessment versus computer-assisted gray scale analysis in the ultrasound evaluation of neonatal respiratory status

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0202397 ◽  
Author(s):  
Francesco Raimondi ◽  
Fiorella Migliaro ◽  
Luisa Verdoliva ◽  
Diego Gragnaniello ◽  
Giovanni Poggi ◽  
...  
Keyword(s):  
Visual Assessment ◽  
Computer Assisted ◽  
Scale Analysis ◽  
Gray Scale ◽  
Respiratory Status ◽  
Ultrasound Evaluation

scholarly journals Nondestructive Measurement of Vegetative Cover Using Digital Image Analysis

HortScience ◽  
2004 ◽  
Vol 39 (1) ◽  
pp. 55-59 ◽  
Author(s):  
Mercy A. Olmstead ◽  
Robert Wample ◽  
Stephanie Greene ◽  
Julie Tarara
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Cover Crops ◽  
Cover Crop ◽  
Digital Image Analysis ◽  
Ground Surface ◽  
Visual Assessment ◽  
Computer Assisted ◽  
Percent Cover ◽  
Vegetative Cover

Traditionally, vegetative cover has been subjectively assessed by visual assessment. However, visual assessment is thought to overestimate percent vegetative cover. Thus, a repeatable method to objectively quantify percent cover is desirable. In two vineyards near Prosser, Wash., the percentage of ground surface covered by up to 15 different cover crops was assessed both visually and by computer-assisted digital image analysis. Quadrats in the cover crop were photographed digitally and the images analyzed with commercially available software. Areas of green vegetation in each image were identified and measured. Weeds in some images were differentiated from the cover crop by user-defined thresholds. Subjective visual estimates of percent vegetative cover were generally higher than those digitally estimated. Values for the visual estimates ranged from 5% to 70% in 1998 (mean = 52.4%) and 7.5% to 55% in 1999 (mean = 30.7%), compared to digital readings ranging from 0.5% to 24% (mean = 11.1%) and 10.3% to 36.6% cover (mean = 20.1%), respectively. The visual assessments had lower coefficients of variability in 1998 (cv 28.1) than the digital image analysis (cv 52.3), but in 1999, the values for the two techniques were similar (cv 41.2 vs. cv 45.7). Despite initial variations between the two methods, the accuracy of digital image analysis for measuring percentage vegetative cover is superior.

scholarly journals Development and deployment of the Computer Assisted Neighborhood Visual Assessment System (CANVAS) to measure health-related neighborhood conditions

2015 ◽  
Vol 31 ◽  
pp. 163-172 ◽  
Author(s):  
Michael D.M. Bader ◽  
Stephen J. Mooney ◽  
Yeon Jin Lee ◽  
Daniel Sheehan ◽  
Kathryn M. Neckerman ◽  
...  
Keyword(s):  
Visual Assessment ◽  
Assessment System ◽  
Computer Assisted ◽  
Neighborhood Conditions ◽  
Health Related

scholarly journals Grayscale Color Mapping with the Mathematical Analysis of an Ultrasound Image in the Differential Diagnosis of Cystic and Solid Breast Masses

2020 ◽  
Vol 101 (3) ◽  
pp. 136-146
Author(s):  
D. V. Pasynkov ◽  
I. A. Egoshin ◽  
A. A. Kolchev ◽  
I. V. Klyushkin ◽  
O. O. Pasynkova
Keyword(s):  
Differential Diagnosis ◽  
Power Doppler ◽  
Ultrasound Image ◽  
Automated Analysis ◽  
Visual Assessment ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Color Mapping ◽  
Malignant Breast ◽  
The Masses

Objective. Atypical breast cysts are often quite a serious problem in noninvasive ultrasound differential diagnosis. To develop a system for automated analysis of grayscale ultrasound images, which on the principles of mathematical processing would make it possible to increase the specificity of diagnosis in this situation.Material and methods. The authors developed the CystChecker 1.0 software package. To test this system, they used a set of 217 ultrasound images: 107 cystic (including 53 atypical lesions that were hardly differentially diagnosed by standard methods) and 110 solid (both benign and malignant) breast masses. All the masses were verified by cytology and/or histology. Visual assessment was carried out analyzing grayscale ultrasound, color/power Doppler, and elastography images.Results. Using the system developed by the authors could correctly identify all (n = 107 (100%)) typical cysts, 107 (97.3%) of 110 solid masses, and 50 (94.3%) of 53 atypical cysts. On the contrary, the standard visual assessment provided a possibility of correctly identifying all (n = 107 (100%)) typical cysts, 96 (87.3%) of 110 solid masses, and 32 (60.4%) of 53 atypical cysts (p < 0.05). The corresponding values of the overall specificity of automated and visual assessments were 98 and 87%, respectively.Conclusion. Using the system developed by the authors for automated analysis provides a higher specificity than the visual assessment of an ultrasound image, which is carried out by a qualified specialist.

scholarly journals Breast elastography: diagnostic performance of computer-aided diagnosis software and interobserver agreement

2020 ◽  
Vol 53 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Eduardo F. C. Fleury ◽  
Karem Marcomini
Keyword(s):  
Breast Imaging ◽  
Interobserver Agreement ◽  
Visual Assessment ◽  
Computer Aided Diagnosis ◽  
Ultrasound Elastography ◽  
Breast Masses ◽  
Cutoff Value ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract Objective: To determine the best cutoff value for classifying breast masses by ultrasound elastography, using dedicated software for strain elastography, and to determine the level of interobserver agreement. Materials and Methods: We enrolled 83 patients with 83 breast masses identified on ultrasound and referred for biopsy. After B-mode ultrasound examination, the lesions were manually segmented by three radiologists with varying degrees of experience in breast imaging, designated reader 1 (R1, with 15 years), reader 2 (R2, with 2 years), and reader 3 (R3, with 8 years). Elastography was performed automatically on the best image with computer-aided diagnosis (CAD) software. Cutoff values of 70%, 75%, 80%, and 90% of hard areas were applied for determining the performance of the CAD software. The best cutoff value for the most experienced radiologists was then compared with the visual assessment. Interobserver agreement for the best cutoff value was determined, as were the interclass correlation coefficient and concordance among the radiologists for the areas segmented. Results: The best cutoff value of the proportion of hard area within a breast mass, for experienced radiologists, was found to be 75%. At a cutoff value of 75%, the interobserver agreement was excellent between R1 and R2, as well as between R1 and R3, and good between R2 and R3. The interclass concordance coefficient among the three radiologists was 0.950. When assessing the segmented areas by size, we found that the level of agreement was higher among the more experienced radiologists. Conclusion: The best cutoff value for a quantitative CAD system to classify breast masses was 75%.

Visual assessment and computer–assisted image analysis of Fusarium head blight in the field to predict mycotoxin accumulation in wheat grains

2017 ◽  
Vol 150 (4) ◽  
pp. 1065-1081 ◽  
Author(s):  
Johann Leplat ◽  
Pierre Mangin ◽  
Laurent Falchetto ◽  
Cécile Heraud ◽  
Elodie Gautheron ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fusarium Head Blight ◽  
Visual Assessment ◽  
Computer Assisted ◽  
Head Blight ◽  
Wheat Grains ◽  
Computer Assisted Image

Comparison of a Visual to a Computer Assisted Technique for Detecting Apoptosis

2005 ◽  
Vol 6 (3) ◽  
pp. 180-186 ◽  
Author(s):  
Caryl Goodyear-Bruch ◽  
Kaycee Simon ◽  
Sandra Hall ◽  
Matthew S. Mayo ◽  
Janet D. Pierce
Keyword(s):  
Visual Perception ◽  
Computer Program ◽  
Interrater Reliability ◽  
Fluorescent Dyes ◽  
Statistical Significance ◽  
Visual Assessment ◽  
Computer Assisted ◽  
Visual Methods ◽  
Counting Method ◽  
Visual Method

In many studies, fluorescent dyes (ethidium bromide [EB] and acridine orange [AO]) are used to stain DNA to determine if nuclei are apoptotic. However, there are numerous visual methods for counting these stained DNA that may lead to inaccuracies Measuring apoptosis by the visual counting method may be imprecise because of the variability of individuals’ perception of color. Therefore, the authors compared a visual method of counting chromatin for apoptosis with a method relying on a computer program. They began counting chromatin using the visual method, in which individuals identify the stained DNA using their own visual perception. For comparison, they used a software-based counting method (analySIS software) to determine the color (hue) of the stained DNA. Using the numeric hue values from the software eliminates the variations in human color perception. Intra and interrater reliability of the visual and computerassisted counting methods were evaluated with Spearman’s. The authors found statistical significance in the intrarater reliability (r = 1.0,P = 0.0001 for all chromatin categories) and interrater reliability (r = 0.975,P = 0.005 for both readings) when using the software program. No statistical significance was found for the visual counting method, indicating inaccuracy between and within raters. Thus, the computerassisted counting method of identifying the damaged DNA is more accurate and precise than the individual’s visual perception of color. Based on these data, apoptosis measurements using color staining with EB and AO should be determined using hue values generated by a computer program and not by a researcher’s visual assessment.

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