Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Fachrurrozi, M.
- Development of Iridology System Database for Colon Disorders Identification using Image Processing
Abstract Views :597 |
PDF Views:393
Authors
Affiliations
1 Lab. Industrial Automation. Department of Computer Engineering, Faculty of Computer Sciences,Universitas Sriwijaya, ID
2 Department of Informatics Engineering, Faculty of computer Sciences, Universitas Sriwijaya Jln. Raya Palembang-Prabumulih km 32. Indralaya. Ogan Ilir. Sumatera Selatan, ID
1 Lab. Industrial Automation. Department of Computer Engineering, Faculty of Computer Sciences,Universitas Sriwijaya, ID
2 Department of Informatics Engineering, Faculty of computer Sciences, Universitas Sriwijaya Jln. Raya Palembang-Prabumulih km 32. Indralaya. Ogan Ilir. Sumatera Selatan, ID
Source
Indian Journal of Bioinformatics and Biotechnology, Vol 2, No 6 (2013), Pagination: 100–103Abstract
Iridology is an art of knowledge to detect a specific disease of human body from the iris. The detection will tell each individual organ when it has low or high performance (abnormal). The iris reveals conditions change of every part of the body. Every organ and part of the body are represented in the iris in a well-defined area. The objective of research is to develop an iris database of colon disorder based on the map of iridology. This map is represented as a diagnosis tool to detect the common colon disorder. In developing the database, Sixty (60) subjects were enrolled in the study where 35 subjects had histologically proven problem in colon disorder, and 25 were control subject. To extract the iris, the image processing methods such Hough transform, segmentation and normalization were applied in this research. The conclusion of this paper is the proposed an iris database for helping medical doctor in detecting colon disorder using image processing.Keywords
Colon Disorder, Iridology, Iris Database, Image ProcessingReferences
- Daugman J (2002). How iris recognition works, Proceedings of 2002 International Conference on Image Processing, vol 1, I-33–I-36.
- Hauser H, Karl J et al. (2000). Information from Structure and Colour, Iridology 1. Heimsheim: Felke Institut, 134–243.
- Jackson P (2004). Practical iridology, Working with the chart, Carrol and Brown Publisher Limited, 76–78.
- Li Y, Wang K et al. (2007). Extracting the autonomic nerve wreath of iris based on an improved snake approach, Neurocomputing, vol 70(4), 743–748.
- Ballard H D (1981). Generalizing the Hough transform to detect arbitrary shapes, Pattern Recognition, vol 13(2), 111–122.
- Cui J, Wang Y et al. (2004). A fast and robust iris localization method based on texture segmentation, Proceedings SPIE, Biometric Technology for Human Identification, vol 5404, doi:10.1117/12.541921.
- Huang J, Wang Y et al. (2004). A new iris segmentation method for recognition, Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, 554–557.
- He Z, Tan T et al. (2009). Toward accurate and fast iris segmentation for iris biometrics, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 31(9), 1670–1684.
- Othman Z, and Prabuwono A S (2010). Preliminary study on iris recognition system: tissues of body organs in iridology, IEEE EMBS conference on Biomedical Engineering and Sciences (IECBES), 115–119.