scholarly journals Contrast dependence of spatial summation revealed by classification image analysis

2004 ◽  
Vol 4 (8) ◽  
pp. 539-539 ◽  
Author(s):  
Y. Morgenstern ◽  
J. H. Elder ◽  
Y. Hou
Keyword(s):  
Image Analysis ◽  
Spatial Summation ◽  
Classification Image

scholarly journals Visual search in noise: Revealing the influence of structural cues by gaze-contingent classification image analysis

10.1167/6.4.7 ◽  
2006 ◽  
Vol 6 (4) ◽  
pp. 7 ◽  
Author(s):  
Umesh Rajashekar ◽  
Alan C. Bovik ◽  
Lawrence K. Cormack
Keyword(s):  
Image Analysis ◽  
Visual Search ◽  
Classification Image

scholarly journals Frequency tuning of shape perception revealed by classification image analysis

2018 ◽  
Vol 18 (8) ◽  
pp. 9 ◽  
Author(s):  
John Wilder ◽  
Ingo Fruend ◽  
James H. Elder
Keyword(s):  
Image Analysis ◽  
Frequency Tuning ◽  
Shape Perception ◽  
Classification Image

scholarly journals Classification image analysis of oriented texture detection

2010 ◽  
Vol 7 (9) ◽  
pp. 359-359
Author(s):  
C. C.-F. Or ◽  
J. H. Elder
Keyword(s):  
Image Analysis ◽  
Classification Image ◽  
Oriented Texture ◽  
Texture Detection

scholarly journals Computational analysis of tongue image for health diagnosis

2017 ◽  
Author(s):  
◽  
Meng Zhang
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Image Segmentation ◽  
The Body ◽  
Support Vector ◽  
Classification Image ◽  
Machine Learning Model ◽  
Machine Learning Applications ◽  
User Friendly ◽  
Traditional Chinese Medical

Tongue, the primary taste organ in the mouth, can reflect the whole body's health conditions based on the Traditional Chinese Medical (TCM) theories. Watching the tongue is one of the most common, essential and reliable methods for the TCM doctor to make diagnoses. In this thesis, a new health system is introduced based on tongue image analysis. The technologies adopted in this system ranged from tongue image processing algorithms to machine learning applications. The tongue image algorithms used in this work include image segmentation, tongue recognition and tongue image classification. Image segmentation was used to get rid of other unrelated parts, such as lip, face and neck, while keeping the tongue only. Then two recognition methods were applied to check whether the segmented result is a tongue or not. For different tongue patterns, the Support Vector Machine is applied to train a machine learning model and make predictions to classify the tongue into different labeled groups. An app named 'iTongue' is designed to monitor the body status by taking and processing tongue images in smart phones. The app provides a user-friendly, fast and powerful health tool based on TCM theories. The whole system is implemented in a webbased environment. An advanced portal was developed to connect the users and the TCM doctors. The users will not only obtain the analysis label of tongue images, but also get some life style recommendations based on the tongue image analysis. This portal helps the user understand more about his body status and guide him to adopt a more suitable diet and improve exercise.

Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

1977 ◽  
Vol 35 ◽  
pp. 220-221
Author(s):  
S.F. Stinson ◽  
J.C. Lilga ◽  
M.B. Sporn
Keyword(s):  
Image Analysis ◽  
Pattern Analysis ◽  
Relative Proportion ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Cross Sectional ◽  
Exfoliated Cells ◽  
Neoplastic Lesions ◽  
Analysis System ◽  
Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

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