scholarly journals Review on Medical Image Retrieval Based on Wavelet, Bag of Features and Relevance Feedback

2020 ◽  
pp. 649-653
Author(s):  
Syed Tanzeem Ahmed ◽  
Dr. Nikhat Raza
Keyword(s):  
Response Time ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Medical Image ◽  
Texture Features ◽  
Similar Type ◽  
Global Level ◽  
Medical Image Retrieval ◽  
Bag Of Features ◽  
Technological Advances

Technological advances have evolved in all the directions including the biomedical, because of which a record number of lives are saved every day. The advancement has now surpassed the tools level, now the doctors with the help of new tools can also detect diseases, which saves the response time. In this paper, we will work on one such technique which will help in retrieving the similar type of images with the help of their features. In this paper, the features such as Texture features, LBP features, Retrieval feature, which are processed with hash coding and relevance feedback to get the final results. The framework provides the output utilizing a hash coding classifiers which predict the image from the database of the images. The images are classified on a global level with the help of multiple low-level features.

Medical Image Retrieval Based on Semi-Supervised Learning

2010 ◽  
Vol 108-111 ◽  
pp. 201-206 ◽  
Author(s):  
Hui Liu ◽  
Cai Ming Zhang ◽  
Hua Han
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Image Retrieval ◽  
Supervised Learning ◽  
Relevance Feedback ◽  
Medical Image ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Learning Support ◽  
Medical Image Retrieval

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

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