The quality of a fingerprint is essential to the performance of AFIS (Automatic Fingerprint Identification System). Such a quality may be classified by clarity and regularity of ridge-valley structures.1,2 One may calculate thickness of ridge and valley to measure the clarity and regularity. However, calculating a thickness is not feasible in a poor quality image, especially, severely damaged images that contain broken ridges (or valleys). In
order to overcome such a difficulty, the proposed approach employs the statistical properties in a local block, which involve the mean and spread of the thickness of both ridge and valley. The mean value is used for determining whether a fingerprint is wet or dry. For example, the black pixels are dominant if a fingerprint is wet, the average thickness of ridge is larger than one of valley, and vice versa on a dry fingerprint. In addition, a standard deviation is used for determining severity of damage. In this study, the quality is divided into three categories based on two statistical properties mentioned above: wet, good, and dry. The number of low quality
blocks is used to measure a global quality of fingerprint. In addition, a distribution of poor blocks is also measured using Euclidean distances between groups of poor blocks. With this scheme, locally condensed poor blocks decreases the overall quality of an image. Experimental results on the fingerprint images captured by
optical devices as well as by a rolling method show the wet and dry parts of image were successfully captured. Enhancing an image by employing morphology techniques that modifying the detected poor quality blocks is illustrated in section 3. However, more work needs to be done on designing a scheme to incorporate the number of poor blocks and their distributions for a global quality.
This paper presents a novel algorithm for locating pupils in a portrait image for ID card application. The proposed algorithm composed of three steps; skin detection, eye detection, and pupil detection. Skin detection for reducing the region of interest employs three modified single Gaussian skin models. In the second step, candidates of horizontal and vertical eye locations are found by utilizing amount of deviation in R channel with an image that is cropped by skin detection. A small block centered at obtained coarse location is then further processed in pupil detection to find a precise pupil location. This step involves Pupil Index that measures the characteristics of pupil. If more than two locations are competing, ratios of Pupil Index and
geometry rules are involved to select pupil locations. Experiments show that the algorithm successfully locates pupils. However, more works may need to be done on images that are rotated
and/or tilted to a high degree.
Proc. SPIE. 3980, Medical Imaging 2000: PACS Design and Evaluation: Engineering and Clinical Issues
KEYWORDS: Image encryption, Digital imaging, Mammography, Medical imaging applications, Computer security, Digital mammography, Asynchronous transfer mode, Network security, Picture Archiving and Communication System, Breast imaging
Tele-medical imaging applications require low cost, and high- speed backbone wide area networks (WAN) to carry large amount of imaging data for rapid turn around interpretation. Current low cost commercially available WAN is too slow for medical imaging applications, while high speed WAN is too costly. Internet2 or Next Generation Internet (NGI) emerges as a good candidate for tele-medical imaging applications because of its high speed and low cost. This paper describes the beginning of a three-year project on exploring the possibility of using NGI for medical imaging applications. Connectivity of a private ATM to the Internet2 is first discussed, followed by methods of preserving data integrity in the public networks. Two medical imaging applications in telemammography and interactive teaching of breast imaging are presented. A preliminary plan on methods of evaluating the performance of the NGI is followed.