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 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.

Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique

2021 ◽  
Vol 14 (2) ◽  
pp. 48-66
Author(s):  
Sneha Kugunavar ◽  
Prabhakar C. J.
Keyword(s):  
Image Retrieval ◽  
Delaunay Triangulation ◽  
Medical Image ◽  
Region Of Interest ◽  
Texture Features ◽  
Ct Images ◽  
Medical Image Retrieval ◽  
Segmentation Technique ◽  
Geometric Moments ◽  
Benchmark Datasets

This article presents a novel technique for retrieval of lung images from the collection of medical CT images. The proposed content-based medical image retrieval (CBMIR) technique uses an automated image segmentation technique called Delaunay triangulation (DT) in order to segment lung organ (region of interest) from the original medical image. The proposed method extracts novel and discriminant features from the segmented lung region instead of extracting novel features from the whole original image. For the extraction of shape features, the authors employ edge histogram descriptor (EHD) and geometric moments (GM), and for the extraction of texture features, the authors use gray-level co-occurrence matrix (GLCM) technique. The shape and texture features are combined to form the hybrid feature which is used for retrieval of similar lung images. The proposed method is evaluated using two benchmark datasets of lung CT images. The simulation results prove that the proposed CBMIR framework shows improved performance in terms of retrieval accuracy and retrieval time.

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