scholarly journals Improved Content Based Medical Image Retrieval using PCA with SURF Features

2019 ◽  
Vol 8 (10S) ◽  
pp. 112-114
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Feature Vector ◽  
Principal Component ◽  
Medical Field ◽  
Huge Number ◽  
Query Image ◽  
Medical Image Retrieval ◽  
Image Retrieval System ◽  
Surf Features

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.

scholarly journals Content-based medical image retrieval system based on gradient orientation and edge information

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
PRANJIT DAS
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Principal Component ◽  
High Resolution Computed Tomography ◽  
Medical Image Retrieval ◽  
Retrieval Rate ◽  
Image Retrieval System ◽  
Gradient Orientation ◽  
Canny Edge ◽  
Computer Based

Retrieval of biomedical pictures is a significant side of computer based diagnosis. It helps the radiologist and restorative authority to spot and analyze the particular disease. This paper proposes a Content Based Medical Image Retrieval (CBMIR) approach for retrieving similar biomedical images. The extraction of retrieving features is based on histogram of oriented gradients (HOG) and canny edge detection. To reduce the dimensionality, principal component analysis(PCA) is employed over the feature vector. The experiments are conducted on high-resolution computed tomography medical images of lungs. With the average retrieval rate (ARR) and average retrieval precision (ARP), the performance of the proposed approach is analyzed and compared with other existing methods viz. Local Binary Pattern (LBP), LBP with uniform patterns (LBPu2), Local Mesh Pattern with uniform patterns (LMePu2) and LMeP with gabor transform (GLMeP).

A NEW METHOD OF MEDICAL IMAGE RETRIEVAL BASED ON COLOR–TEXTURE CORRELOGRAM AND GTI MODEL

2009 ◽  
Vol 08 (02) ◽  
pp. 239-248 ◽  
Author(s):  
XIAO-YING TAI ◽  
LI-DONG WANG ◽  
QIN CHEN ◽  
REN FUJI ◽  
KITA KENJI
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Feature Vector ◽  
Texture Feature ◽  
Image Feature ◽  
Similarity Metric ◽  
Medical Image Retrieval ◽  
Endoscopic Image ◽  
Color Correlogram ◽  
Color Texture

This paper presents a method for endoscopic image retrieval based on color–texture correlogram and Generalized Tversky's Index (GTI) model. First we define a new image feature named color–texture correlogram, which is the extension of color correlogram. The texture image extracted by texture spectrum algorithm is combined with color feature vector, and then we calculate the spatial correlation of color–texture feature vector. Similarity metric is also the key technology during domain of image retrieval, GTI model is used in medical image retrieval for similarity metric, and the technique of relevance feedback is used in the algorithm to enhance the efficiency of retrieval. Experimental results show that the method discussed in this paper is much more effective.

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