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

2018 ◽  
Vol 15 (3) ◽  
pp. 517-531 ◽  
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.

scholarly journals Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence

2019 ◽  
Vol 8 (4) ◽  
pp. 462 ◽  
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Jiho Choi ◽  
Kang Ryoung Park
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Diagnosis And Treatment ◽  
Imaging Modalities ◽  
Imaging Data ◽  
Medical Database ◽  
Medical Image Retrieval

Medical-image-based diagnosis is a tedious task‚ and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can adversely affect medical treatment. However, this problem can be resolved by exploring similar cases in the previous medical database through an efficient content-based medical image retrieval (CBMIR) system. In the past few years, heterogeneous medical imaging databases have been growing rapidly with the advent of different types of medical imaging modalities. Recently, a medical doctor usually refers to various types of imaging modalities all together such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray, and ultrasound, etc of various organs in order for the diagnosis and treatment of specific disease. Accurate classification and retrieval of multimodal medical imaging data is the key challenge for the CBMIR system. Most previous attempts use handcrafted features for medical image classification and retrieval, which show low performance for a massive collection of multimodal databases. Although there are a few previous studies on the use of deep features for classification, the number of classes is very small. To solve this problem, we propose the classification-based retrieval system of the multimodal medical images from various types of imaging modalities by using the technique of artificial intelligence, named as an enhanced residual network (ResNet). Experimental results with 12 databases including 50 classes demonstrate that the accuracy and F1.score by our method are respectively 81.51% and 82.42% which are higher than those by the previous method of CBMIR (the accuracy of 69.71% and F1.score of 69.63%).

scholarly journals An Enhanced Framework for Content Based Medical Image Retrieval using Deep Neural Network

2019 ◽  
Vol 8 (5S3) ◽  
pp. 239-244
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Low Cost ◽  
Image Features ◽  
Semantic Gap ◽  
Medical Image Retrieval ◽  
Large Size ◽  
Image Repository ◽  
High Level ◽  
Technology Growth

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.

scholarly journals Triplet Contents based Medical Image Retrieval System for Lung Nodules CT Images Retrieval and Recognition Application

2019 ◽  
Vol 8 (6) ◽  
pp. 3132-3143
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Medical Image ◽  
Recognition Rate ◽  
Ct Images ◽  
User Preferences ◽  
Lung Nodules ◽  
Discrete Wavelet ◽  
Semantic Features ◽  
Medical Image Retrieval

Content Based Medical Image Retrieval (CBMIR) has found its relevance in medical diagnosis by processing massive medical databases based on visual and semantic features and user preferences. In this paper we address two issues such as retrieval and recognition. We present a novel method called Triplet-CBMIR for lung nodules CT images retrieval and recognition application. A Triplet CBMIR is a combination of three properties: Visual Features (Shape and Texture), Semantic Features and Relevance Feedback. Dataset training is done using: Preprocessing, Feature Extraction, Selection, Nodules Sign Detection and Clustering. In preprocessing we perform image scaling, denoising and normalization. In feature extraction, two methods are presented such as Hybrid Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), Bounding-Box based Convolutional Network (CNN) for visual and semantic features extraction. Then optimum set of feature vectors are selected using Mutual Information based Neighborhood Entropy (MINε). Based on selected features, lung nodule sign is detected using K-nearest Neighbor (KNN) algorithm in which Hassanat Distance used and similar images are grouped using Multi-Self organizing Map (SOM). For similarity measurement, d_1 distance metric is used. Benchmark dataset such as LISS and LIDC are used for the study. Performance matrices such as Average Precision Rate (APR), Average Retrieval Rate (ARR), Average Recognition Rate (ArR), Running Time found in the simulation results are compared with some other already present state-of-the-art works. The proposed method shows a significant improvement as compared to other existing methods.

scholarly journals Deep triplet hashing network for case-based medical image retrieval

2021 ◽  
Vol 69 ◽  
pp. 101981
Author(s):  
Jiansheng Fang ◽  
Huazhu Fu ◽  
Jiang Liu
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Image Retrieval ◽  
Case Based
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