Enhancement of classification accuracy of our Adaptive Classifier using image processing techniques in the field of Medical Data Mining

2015 ◽  
Author(s):  
Sneha Chandra ◽  
Maneet Kaur
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Classification Accuracy ◽  
Medical Data ◽  
Medical Data Mining ◽  
Image Processing Techniques ◽  
Processing Techniques

Surgeon Assistive Augmented Reality Model with the use of Endoscopic Camera for Line of Vision Calculation

2013 ◽  
pp. 658-674
Author(s):  
Anastasia Daskalaki ◽  
Kostas Giokas ◽  
Dimitris Koutsouris
Keyword(s):  
Image Processing ◽  
Augmented Reality ◽  
Situation Awareness ◽  
User Interfaces ◽  
Medical Data ◽  
Graphical User Interfaces ◽  
Ar Model ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Robotic Surgeries

In this paper, the authors describe a surgeon assistive Augmented Reality (AR) model for endoscopic procedures. They analyze the main parts of the model and the processes that need to be established such as, the registration of the patient, the segmentation of medical data, their 3D reconstruction, and the detection of endoscopic instruments and the camera. The authors present two graphical user interfaces, build to serve the needs of segmentation, navigation, and visualization of the final intra-operative scene. By using preoperative data of the patient (MRI-CT) and image processing techniques, the authors can provide a unique view of the surgical scene. The potentials and the advantages of endoscopic-robotic surgeries nowadays can be improved. Augmented surgery scenes with information about the patients underline structures, enables wider situation awareness, precision, and confidence.

Surgeon Assistive Augmented Reality Model with the use of Endoscopic Camera for Line of Vision Calculation

2012 ◽  
Vol 1 (4) ◽  
pp. 25-42
Author(s):  
Anastasia Daskalaki ◽  
Kostas Giokas ◽  
Dimitris Koutsouris
Keyword(s):  
Image Processing ◽  
Augmented Reality ◽  
Situation Awareness ◽  
User Interfaces ◽  
Medical Data ◽  
Graphical User Interfaces ◽  
Ar Model ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Robotic Surgeries

In this paper, the authors describe a surgeon assistive Augmented Reality (AR) model for endoscopic procedures. They analyze the main parts of the model and the processes that need to be established such as, the registration of the patient, the segmentation of medical data, their 3D reconstruction, and the detection of endoscopic instruments and the camera. The authors present two graphical user interfaces, build to serve the needs of segmentation, navigation, and visualization of the final intra-operative scene. By using preoperative data of the patient (MRI-CT) and image processing techniques, the authors can provide a unique view of the surgical scene. The potentials and the advantages of endoscopic-robotic surgeries nowadays can be improved. Augmented surgery scenes with information about the patients underline structures, enables wider situation awareness, precision, and confidence.

scholarly journals Detecting Keratoconus by Using SVM and Decision Tree Classifiers with the Aid of Image Processing

2019 ◽  
Vol 16 (4(Suppl.)) ◽  
pp. 1022
Author(s):  
Mosa Et al.
Keyword(s):  
Image Processing ◽  
Classification Accuracy ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Methods ◽  
Health It ◽  
Image Processing Techniques ◽  
Learning Techniques ◽  
Medical Instruments ◽  
Processing Techniques

 Researchers used different methods such as image processing and machine learning techniques in addition to medical instruments such as Placido disc, Keratoscopy, Pentacam;to help diagnosing variety of diseases that affect the eye. Our paper aims to detect one of these diseases that affect the cornea, which is Keratoconus. This is done by using image processing techniques and pattern classification methods. Pentacam is the device that is used to detect the cornea’s health; it provides four maps that can distinguish the changes on the surface of the cornea which can be used for Keratoconus detection. In this study, sixteen features were extracted from the four refractive maps along with five readings from the Pentacam software. The classifiers utilized in our study are Support Vector Machine (SVM) and Decision Trees classification accuracy was achieved 90% and 87.5%, respectively of detecting Keratoconus corneas. The features were extracted by using the Matlab (R2011 and R 2017) and Orange canvas (Pythonw).       

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