Computer assisted uroflowmetry diagnostic system

2006 ◽  
Author(s):  
Jarosław Makal ◽  
Adam Idźkowski ◽  
Wojciech Walendziuk
Keyword(s):  
Diagnostic System ◽  
Computer Assisted

scholarly journals A new computer-assisted automated diagnostic system(ML 1100) of exercise electrocardiography.

1996 ◽  
Vol 16 (4) ◽  
pp. 369-375
Author(s):  
Kiyoshi Kawakubo ◽  
Toshiki Ohta ◽  
Haruki Musya ◽  
Tohru Hashimoto ◽  
Mutuo Kaneko ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Computer Assisted ◽  
Exercise Electrocardiography

A Computer Assisted Diagnostic System for Mass Detection

1998 ◽  
pp. 79-86 ◽  
Author(s):  
Wei Qian ◽  
Lihua Li ◽  
Laurence Clarke ◽  
Fei Mao ◽  
Robert A. Clark ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Computer Assisted ◽  
Mass Detection

scholarly journals Localization and segmentation of optic disc in retinal images using Circular Hough transform and Grow Cut algorithm

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2003 ◽  
Author(s):  
Muhammad Abdullah ◽  
Muhammad Moazam Fraz ◽  
Sarah A. Barman
Keyword(s):  
Optic Disc ◽  
Hough Transform ◽  
Retinal Image ◽  
Diagnostic System ◽  
Retinal Images ◽  
Computer Assisted ◽  
Morphological Operations ◽  
Circular Hough Transform ◽  
Disc Center ◽  
Unique Method

Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the Circular Hough transform and the Grow Cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the Circular Hough transform, and the Grow Cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.

DIAG: A computer-assisted dermatologic diagnostic system—clinical experience and insight

1985 ◽  
Vol 12 (1) ◽  
pp. 132-143 ◽  
Author(s):  
Herbert F. Haberman ◽  
Kenneth H. Norwich ◽  
D.L. Diehl ◽  
Stephen J. Evans ◽  
Bart Harvey ◽  
...  
Keyword(s):  
Clinical Experience ◽  
Diagnostic System ◽  
Computer Assisted
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