Medical image retrieval based on low level feature and high level semantic feature

As the technology growth fuelled by low cost tech in the areas of compute, storage the need for faster retrieval and processing of data is becoming paramount for organizations. The medical domain predominantly for medical image processing with large size is critical for making life critical decisions. Healthcare community relies upon technologies for faster and accurate retrieval of images. Traditional, existing problem of efficient and similar medical image retrieval from huge image repository are reduced by Content Based Image Retrieval (CBIR) . The major challenging is an semantic gap in CBIR system among low and high level image features. This paper proposed, enhanced framework for content based medical image retrieval using DNN to overcome the semantic gap problem. It is outlines the steps which can be leveraged to search the historic medical image repository with the help of image features to retrieve closely relevant historic image for faster decision making from huge volume of database. The proposed system is assessed by inquisitive amount of images and the performance efficiency is calculated by precision and recall evaluation metrics. Experimental results obtained the retrieval accuracy is 79% based on precision and recall and this approach is preformed very effectively for image retrieval performance.

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Convolutional neural networks and hash learning for feature extraction and of fast retrieval of pulmonary nodules

Computer Science and Information Systems ◽

10.2298/csis171210020q ◽

2018 ◽

Vol 15 (3) ◽

pp. 517-531 ◽

Cited By ~ 2

Author(s):

Pinle Qin ◽

Jun Chen ◽

Kai Zhang ◽

Rui Chai

Keyword(s):

Image Retrieval ◽

Visual Information ◽

Medical Image ◽

Medical Knowledge ◽

Pulmonary Nodules ◽

Semantic Features ◽

Imaging Data ◽

Medical Image Retrieval ◽

Segmented Images ◽

High Level

With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the ?semantic gap? that exists between the low level visual information captured by imaging devices and high level semantic information perceived by the human. Using deep convolution neural network (CNN) to construct the CBMIR system can fully characterize the high level semantic features information for medical image retrieval. The existing network mostly used for the natural images can?t produce a good result directly applied to medical image. This paper used UNet method to preprocessing under the guidance of medical knowledge. Then, multi-scale receiving field convolution module is used to extract features of the segmented images with different sizes. Finally, encoded the features and used a coarse to fine search strategy with an average search accuracy of 0.73.

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