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).

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.

A Comparative Study of Medical Image Retrieval Using Distance, Transform, Texture, and Shape

2019 ◽  
pp. 228-262
Author(s):  
A. Swarnambiga ◽  
Vasuki S.
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Distance Transform ◽  
Image Search ◽  
Medical Image Retrieval ◽  
The Third ◽  
Retrieval Rate ◽  
Large Databases ◽  
Otsu Method ◽  
Canny Edge

Content-based medical image retrieval (CBMIR) is the application of computer vision techniques to the problem of medical image search in large databases. Three main techniques are applied to check the applicability. The first technique implemented is distance metrics-based retrieval. The second technique implemented is transform-based retrieval. The transform which has lesser performance is combined with higher performance, to check the applicability of the results. The third technique implemented is content-based medical image retrieval. Texture and shape-based retrieval techniques are also applied. Shape-based retrieval is processed using canny edge with the Otsu method. The multifeature-based technique is also applied and analyzed. The best retrieval rate is achieved by multifeature-based retrieval with 100/50%. Based on more relevant retrieved images all the three, brain, liver, and knee, images are found to be retrieved more with 100/50%.

Medical Image Retrieval System Based on Gabor Wavelet

2006 ◽  
Vol 1 (1) ◽  
pp. 59-63
Author(s):  
Grace Selvarani A ◽  
◽  
Annadurai S ◽  
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Retrieval System ◽  
Gabor Wavelet ◽  
Medical Image Retrieval ◽  
Image Retrieval System
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