Cloud Burst
An improved pre-processing technique with image mining approach for the medical image (Found in Endoscopy) classification.
Rational and Significance
The proposed system mainly concentrates on the diagnosis of Endoscopy Images . This work gives the Endoscopy Surgeons a second option for the easy identification of interior images of esophagus. The important data mining concept that has been included in the proposed work consists of pre-processing of the Endoscopy Images. The method used for pre-processing includes Shape priori technique. The feature selection from the image has been done using the association rule mining. The rules generated for extracted features are stored in the transactional database have been classified using the data mining concept called Decision Tree Classification. The combination of both the association rule mining and the decision tree classification gives the high degree of accuracy and efficiency for the proposed system.
Literature Review
1.A data mining algorithmic approach for processing wireless capsule endoscopy data sets
(Karargyris, A.; Bourbakis, N.
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE )
Wireless capsule endoscopy (WCE) has been a breakthrough in recent medical technology. It is used to view the gastrointestinal tract and detect abnormalities such as bleeding, Crohn's disease, peptic ulcers, and colon cancer. In this paper data mining techniques are utilized to extract useful information from a dataset of abnormal regions and non-abnormal regions. More specifically, the dataset contains polyps regions, ulcers regions and healthy regions. A number of features (shape descriptors, texture descriptors and color information) has been extracted for these regions and using a data mining toolbox useful conclusions are given on various relationships between these regions.
2.Research and application of CT image mining based on rough sets theory and
Rational and Significance
The proposed system mainly concentrates on the diagnosis of Endoscopy Images . This work gives the Endoscopy Surgeons a second option for the easy identification of interior images of esophagus. The important data mining concept that has been included in the proposed work consists of pre-processing of the Endoscopy Images. The method used for pre-processing includes Shape priori technique. The feature selection from the image has been done using the association rule mining. The rules generated for extracted features are stored in the transactional database have been classified using the data mining concept called Decision Tree Classification. The combination of both the association rule mining and the decision tree classification gives the high degree of accuracy and efficiency for the proposed system.
Literature Review
1.A data mining algorithmic approach for processing wireless capsule endoscopy data sets
(Karargyris, A.; Bourbakis, N.
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE )
Wireless capsule endoscopy (WCE) has been a breakthrough in recent medical technology. It is used to view the gastrointestinal tract and detect abnormalities such as bleeding, Crohn's disease, peptic ulcers, and colon cancer. In this paper data mining techniques are utilized to extract useful information from a dataset of abnormal regions and non-abnormal regions. More specifically, the dataset contains polyps regions, ulcers regions and healthy regions. A number of features (shape descriptors, texture descriptors and color information) has been extracted for these regions and using a data mining toolbox useful conclusions are given on various relationships between these regions.
2.Research and application of CT image mining based on rough sets theory and