Brain Medical Image Retrieval Using Non-Negative Matrix Factorization and Canny Edge Detection

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).

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A Proficient Hybrid Framework for Image Retrieval

Applications of Artificial Intelligence for Smart Technology - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3335-2.ch014 ◽

2021 ◽

pp. 206-219

Author(s):

Rajkumar Soundrapandiyan ◽

Ramani Selvanambi

Keyword(s):

Edge Detection ◽

Image Retrieval ◽

Chi Square ◽

Canny Edge Detection ◽

Information Base ◽

Hybrid Framework ◽

Image Retrieval System ◽

Canny Edge ◽

Main Factors ◽

Edge Recognition

In this work, an image retrieval system based on three main factors is constructed. The proposed system at first chooses relevant pictures from an enormous information base utilizing colour moment data. Accordingly, canny edge recognition and local binary pattern and strategies are utilized to remove the texture plus edge separately, as of the uncertainty and resultant pictures of the underlying phase of the system. Afterward, the chi-square distance between the red-green and the blue colour channels of the query and the main image are calculated. Then these two (the LBP pattern and the edge feature extracted from the canny edge detection and by chi-square method) data about these two highlights compared to the uncertainty and chosen pictures are determined and consolidated, are then arranged and the nearest ‘n' images are presented. Two datasets, Wang and the Corel databases, are used in this work. The results shown herein are obtained using the Wang dataset. The Wang dataset contains 1,000 images and Corel contains 10,000 images.

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An Improved Region Growing Method for Medical Image Selection and Evaluation Based on Canny Edge Detection

2011 International Conference on Management and Service Science ◽

10.1109/icmss.2011.5999180 ◽

2011 ◽

Cited By ~ 4

Author(s):

Hong-rui Wang ◽

Jian-li Yang ◽

Hai-jun Sun ◽

Dong Chen ◽

Xiu-ling Liu

Keyword(s):

Edge Detection ◽

Medical Image ◽

Region Growing ◽

Canny Edge Detection ◽

Canny Edge

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

Medical Image Processing for Improved Clinical Diagnosis - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-5876-7.ch011 ◽

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%.

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