Open AccessImproved Content Based Medical Image Retrieval using PCA with SURF Features
Author(s) -
S. Govindaraju,
B. Mukunthan
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j1020.08810s19
Subject(s) - image retrieval , computer science , content based image retrieval , feature (linguistics) , artificial intelligence , pattern recognition (psychology) , scale invariant feature transform , feature detection (computer vision) , feature vector , principal component analysis , metric (unit) , feature extraction , field (mathematics) , similarity (geometry) , image (mathematics) , computer vision , information retrieval , image processing , mathematics , linguistics , philosophy , operations management , pure mathematics , economics
In the computer era, the Content Based Image Retrieval system (CBIR) has most widely used in medical field and crime invention. During the last decade, CBIR emerged as powerful tool to efficiently retrieved images visually similar to query image. The basic process behind this concept is representation of image as feature vector and to measure the similarities between the images with distance between their corresponding feature vectors according to some metrics. The finding of correct features to represent images with, as well as the similarity metric that groups visually similar image together, are important milestone in construction of any CBIR system .The work in this paper focused on retrieve the correct query image from a huge number of medical image databases with the help of Principal Component Analysis (PCA) through SURF feature vector detection. The combination of this method produces an accurate and quick response than other conventional methods like SIFT and SURF feature vector based medical image retrieval.