Semantic Analysis of 3D Anatomical Medical Images for Sub-image Retrieval

2012 ◽  
pp. 139-151 ◽  
Author(s):  
Vikram Venkatraghavan ◽  
Sohan Ranjan
Keyword(s):  
Image Retrieval ◽  
Semantic Analysis ◽  
Medical Images

scholarly journals Medical Image Retrieval: A Multimodal Approach

2014 ◽  
Vol 13s3 ◽  
pp. CIN.S14053 ◽  
Author(s):  
Yu Cao ◽  
Shawn Steffey ◽  
Jianbiao He ◽  
Degui Xiao ◽  
Cui Tao ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Image Retrieval ◽  
Medical Image ◽  
Semantic Analysis ◽  
Medical Images ◽  
Image Data ◽  
Probabilistic Latent Semantic Analysis ◽  
Medical Image Retrieval ◽  
Digital Format ◽  
Deep Boltzmann Machine

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system.

scholarly journals Local Derivative Vector Pattern: Hybrid Pattern for Content-Based Medical Image Retrieval

2020 ◽  
Vol 7 (4) ◽  
pp. 79-86
Author(s):  
Nagadevi Darapureddy ◽  
Nagaprakash Karatapu ◽  
Tirumala Krishna Battula
Keyword(s):  
Image Retrieval ◽  
Texture Analysis ◽  
Medical Image ◽  
Local Binary Pattern ◽  
Medical Images ◽  
Texture Classification ◽  
Medical Image Retrieval ◽  
Local Vector ◽  
Local Derivative ◽  
Local Derivative Pattern

This paper examines a hybrid pattern i.e. Local derivative Vector pattern and comparasion of this pattern over other different patterns for content-based medical image retrieval. In recent years Pattern-based texture analysis has significant popularity for a variety of tasks like image recognition, image and texture classification, and object detection, etc. In literature, different patterns exist for texture analysis. This paper aims at forming a hybrid pattern compared in terms of precision, recall and F1-score with different patterns like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Completed Local Binary Pattern (CLBP), Local Tetra Pattern (LTrP), Local Vector Pattern (LVP) and Local Anisotropic Pattern (LAP) which were applied on medical images for image retrieval. The proposed method is evaluated on different modalities of medical images. The results of the proposed hybrid pattern show biased performance compared to the state-of-the-art. So this can further extended with other pattern to form a hybrid pattern.

scholarly journals Identifying concepts from medical images via transfer learning and image retrieval

2019 ◽  
Vol 16 (4) ◽  
pp. 1978-1991 ◽  
Author(s):  
Xuwen Wang ◽  
◽  
Yu Zhang ◽  
Zhen Guo ◽  
Jiao Li
Keyword(s):  
Image Retrieval ◽  
Transfer Learning ◽  
Medical Images

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 A SELF-BALANCED CLUSTERING TREE FOR SEMANTIC-BASED IMAGE RETRIEVAL

2020 ◽  
Vol 36 (1) ◽  
pp. 49-67
Author(s):  
Nguyen Thi Uyen Nhi ◽  
Van The Thanh ◽  
Le Manh Thanh
Keyword(s):  
Information System ◽  
Data Structure ◽  
Image Retrieval ◽  
Semantic Analysis ◽  
Query Language ◽  
Image Feature ◽  
Retrieval Accuracy ◽  
High Level ◽  
Clustering Tree ◽  
Balanced Clustering

The image retrieval and semantic extraction play an important role in the multimedia systems such as geographic information system, hospital information system, digital library system, etc. Therefore, the research and development of semantic-based image retrieval (SBIR) systems have become extremely important and urgent. Major recent publications are included covering different aspects of the research in this area, including building data models, low-level image feature extraction, and deriving high-level semantic features. However, there is still no general approach for semantic-based image retrieval (SBIR), due to the diversity and complexity of high-level semantics. In order to improve the retrieval accuracy of SBIR systems, our focus research is to build a data structure for finding similar images, from that retrieving its semantic. In this paper, we proposed a data structure which is a self-balanced clustering tree named C-Tree.  Firstly, a method of visual semantic analysis relied on visual features and image content is proposed on C-Tree. The building of this structure is created based on a combination of methods including hierarchical clustering and partitional clustering. Secondly, we design ontology for the image dataset and create the SPARQL (SPARQL Protocol and RDF Query Language) query by extracting semantics of image. Finally, the semantic-based image retrieval on C-Tree (SBIR\_CT) model is created hinging on our proposal. The experimental evaluation 20,000 images of ImageCLEF dataset indicates the effectiveness of the proposed method. These results are compared with some of recently published methods on the same dataset and demonstrate that the proposed method improves the retrieval accuracy and efficiency.

A Content-Based Approach to Medical Images Retrieval

2013 ◽  
Vol 8 (2) ◽  
pp. 15-27
Author(s):  
Mana Tarjoman ◽  
Emad Fatemizadeh ◽  
Kambiz Badie
Keyword(s):  
Image Retrieval ◽  
Fuzzy Inference ◽  
Medical Images ◽  
Magnetic Resonance Images ◽  
Image Features ◽  
Medical Decision ◽  
Content Based Image Retrieval ◽  
Inference System ◽  
Medical Decision Support ◽  
Neuro Fuzzy

Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency.

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