scholarly journals Image analysis and statistical evaluation of two-dimensional human eye retina images of healthy and glaucomatous eyes

2003 ◽  
Author(s):  
Frantisek Pluhacek ◽  
Jaroslav Pospisil
Keyword(s):  
Image Analysis ◽  
Statistical Evaluation ◽  
Two Dimensional ◽  
Human Eye ◽  
Eye Retina ◽  
Retina Images

Research on registration algorithm based on feature points for human eye retina images

2009 ◽  
Author(s):  
Yuran Liu ◽  
Huizhen Yang ◽  
Liyun Su ◽  
Yudong Zhang ◽  
Xuejun Rao
Keyword(s):  
Feature Points ◽  
Human Eye ◽  
Registration Algorithm ◽  
Eye Retina ◽  
Retina Images

A New System For Optomanual Semiautomatic Quantitative Image Analysis

1977 ◽  
Vol 35 ◽  
pp. 210-211
Author(s):  
H.P. Rohr
Keyword(s):  
Image Analysis ◽  
Volume Density ◽  
List Type ◽  
Distribution Analysis ◽  
Type Number ◽  
Point Counting ◽  
Human Eye ◽  
Quantitative Image ◽  
Point Density ◽  
Electronic Counting

Today, in image analysis the broadest possible rationalization and economization have become desirable. Basically, there are two approaches for image analysis: The image analysis through the so-called scanning methods which are usually performed without the human eye and the systems of optical semiautomatic analysis completely relying on the human eye.The new MOP AM 01 opto-manual system (fig.) represents one of the very promising approaches in this field. The instrument consists of an electronic counting and storing unit, which incorporates a microprocessor and a keyboard for choice of measuring parameters, well designed for easy use.Using the MOP AM 01 there are three possibilities of image analysis:the manual point counting,the opto-manual point counting andthe measurement of absolute areas and/or length (size distribution analysis included).To determine a point density for the calculation of the corresponding volume density the intercepts lying within the structure are scanned with the light pen.

scholarly journals What is unique about the human eye? Comparative image analysis on the external eye morphology of human and nonhuman great apes

2021 ◽  
Author(s):  
Fumihiro Kano ◽  
Takeshi Furuichi ◽  
Chie Hashimoto ◽  
Christopher Krupenye ◽  
Jesse G Leinwand ◽  
...  
Keyword(s):  
Image Analysis ◽  
Eye Gaze ◽  
Great Apes ◽  
Gaze Direction ◽  
Human Eye ◽  
The Gaze ◽  
Eye Morphology ◽  
New Findings ◽  
Heated Debate ◽  
Signaling Hypothesis

The gaze-signaling hypothesis and the related cooperative-eye hypothesis posit that humans have evolved special external eye morphology, including exposed white sclera (the white of the eye), to enhance the visibility of eye-gaze direction and thereby facilitate conspecific communication through joint-attentional interaction and ostensive communication. However, recent quantitative studies questioned these hypotheses based on new findings that humans are not necessarily unique in certain eye features compared to other great ape species. Therefore, there is currently a heated debate on whether external eye features of humans are distinguished from those of other apes and how such distinguished features contribute to the visibility of eye-gaze direction. This study leveraged updated image analysis techniques to test the uniqueness of human eye features in facial images of great apes. Although many eye features were similar between humans and other species, a key difference was that humans have uniformly white sclera which creates clear visibility of both eye outline and iris; the two essential features contributing to the visibility of eye-gaze direction. We then tested the robustness of the visibility of these features against visual noises such as darkening and distancing and found that both eye features remain detectable in the human eye, while eye outline becomes barely detectable in other species under these visually challenging conditions. Overall, we identified that humans have distinguished external eye morphology among other great apes, which ensures robustness of eye-gaze signal against various visual conditions. Our results support and also critically update the central premises of the gaze-signaling hypothesis.

Cancer Cell Image Analysis and Visualization

2012 ◽  
pp. 233-243
Author(s):  
Tae-Yun Kim ◽  
Hae-Gil Hwang ◽  
Heung-Kook Choi
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Case Studies ◽  
Cancer Cell ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Dimensional Image ◽  
The Past ◽  
Cell Image ◽  
Cell Image Analysis

We review computerized cancer cell image analysis and visualization research over the past 30 years. Image acquisition, feature extraction, classification, and visualization from two-dimensional to three-dimensional image algorithms are introduced with case studies of bladder, prostate, breast, and renal carcinomas.

scholarly journals Automated three-dimensional measurements of version, inclination, and subluxation

2019 ◽  
Vol 12 (1) ◽  
pp. 31-37
Author(s):  
Dave R Shukla ◽  
Richard J McLaughlin ◽  
Julia Lee ◽  
Ngoc Tram V Nguyen ◽  
Joaquin Sanchez-Sotelo
Keyword(s):  
Computed Tomography ◽  
Image Analysis ◽  
Three Dimensional ◽  
Automated Image Analysis ◽  
Two Dimensional ◽  
Analysis Software ◽  
Glenoid Version ◽  
Image Analysis Software ◽  
Planning Software ◽  
Dimensional Measurements

Background Preoperative planning software has been developed to measure glenoid version, glenoid inclination, and humeral head subluxation on computed tomography (CT) for shoulder arthroplasty. However, most studies analyzing the effect of glenoid positioning on outcome were done prior to the introduction of planning software. Thus, measurements obtained from the software can only be extrapolated to predict failure provided they are similar to classic measurements. The purpose of this study was to compare measurements obtained using classic manual measuring techniques and measurements generated from automated image analysis software. Methods Ninety-five two-dimensional computed tomography scans of shoulders with primary glenohumeral osteoarthritis were measured for version according to Friedman method, inclination according to Maurer method, and subluxation according to Walch method. DICOM files were loaded into an image analysis software (Blueprint, Wright Medical) and the output was compared with values obtained manually using a paired sample t-test. Results Average manual measurements included 13.8° version, 13.2° inclination, and 56.2% subluxation. Average image analysis software values included 17.4° version (3.5° difference, p < 0.0001), 9.2° inclination (3.9° difference, p < 0.001), and 74.2% for subluxation (18% difference, p < 0.0001). Conclusions Glenoid version and inclination values from the software and manual measurement on two-dimensional computed tomography were relatively similar, within approximately 4°. However, subluxation measurements differed by approximately 20%.

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