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 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%).

Content Based Medical Image Retrieval Using Multilevel Hybrid Clustering Segmentation with Feed Forward Neural Network

2020 ◽  
Vol 17 (12) ◽  
pp. 5550-5562
Author(s):  
R. Inbaraj ◽  
G. Ravi
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Large Data ◽  
The Body ◽  
Content Based Image Retrieval ◽  
Multidimensional Data ◽  
Data Sets ◽  
Clinical Images ◽  
Medical Image Retrieval

Content-Based Image Retrieval (CBIR) is another yet broadly recognized method for distinguishing images from monstrous and unannotated image databases. With the improvement of network and mixed media headways ending up being increasingly famous, customers are not content with the regular information retrieval progresses. So nowadays, Content-Based Image Retrieval (CBIR) is the perfect and fast recovery source. Lately, various strategies have been created to improve CBIR execution. Data clustering is an overlooked method of hiding formatting extraction from large data blocks. With large data sets, there is a possibility of high dimensionality Models are a challenging domain with both massive numerical accuracy and efficiency for multidimensional data sets. The calibration and rich information dataset contain the problem of recovery and handling of medical images. Every day, more medical images were converted to digital format. Therefore, this work has applied these data to manage and file a novel approach, the “Clustering (MHC) Approach Using Content-Based Medical Image Retrieval Hybrid.” This work is implemented as four levels. With each level, the effectiveness of job retention is improved. Compared to some of the existing works that are being done in the analysis of this work’s literature, the results of this work are compared. The classification and learning features are used to retrieve medical images in a database. The proposed recovery system performs better than the traditional approach; with precision, recall, F-measure, and accuracy of proposed method are 97.29%, 95.023%, 4.36%, and 98.55% respectively. The recommended approach is most appropriate for recuperating clinical images for various parts of the body.

scholarly journals Modified Location Model Estimation using Content Based Medical Image Retrieval

2019 ◽  
pp. 36-45
Author(s):  
Sachin Kumar ◽  
Krishna Prasad K.
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Location Model ◽  
Human Beings ◽  
Medical Image Retrieval ◽  
Suggested Technique ◽  
Recovery Scheme ◽  
Artificial Neural Network Ann

Image has become more and more difficult to process for human beings. Perfect results cannot be obtained through Content Based Medical Image Retrieval (CBMIR). The CBMIR was implemented to find order effectively retrieve the picture from an enormous database. Deep learning has taken Artificial Intelligence (AI) at an unprecedented rate through revolution and infiltration in the medical field. It has access to vast quantities of information computing energy of effective algorithms of Machine Learning (ML). It enables Artificial Neural Network (ANN) to attain outcomes nearly every Deep Learning (DL) problems. It helps ANN to achieve results everywhere. It is a difficult task to obtain medical images from an anatomically diff dataset. The goal of the research is to automate the medical image recovery scheme that incorporates subject and place probabilities to improve efficiency. It is suggested to integrate the different data or phrases into a DL location model. It is also measuring a fresh metric stance called weighted accuracy (wPrecision). The experiment will be conducted on two big medical image datasets revealing that the suggested technique outperforms current medical imaging technologies in terms of accuracy and mean accuracy. The CBMIR have about 8,000 pictures, the proposed technique will attain excellent precision (nearly 90 percent). The proposed scheme will attain greater precision for the top ten pictures (97.5 percent) as compared to the last CBMIR recovery technologies with 15,000 picture dataset. It will assist doctors with better accuracy in obtaining medical images.

scholarly journals Medical Image Retrieval using Two Dimensional PCA

2020 ◽  
Vol 9 (4) ◽  
pp. 1852-1856
Keyword(s):  
Image Analysis ◽  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Principal Component ◽  
Image Databases ◽  
Two Dimensional ◽  
Medical Image Retrieval ◽  
Simulation Results

Medical image analysis will be used to develop image retrieval system to provide access to image databases using extracted features. Content Based Image Retrieval (CBIR) is used for retrieving similar images from image databases. During the last few years, medical images are grown and used for medical image analysis. Here, we are proposed that medical image retrieval using two dimensional Principal Component Analysis (2DPCA). For extracting medical image features, 2DPCA has advantageous that evaluates accurate covariance matrix easily as much smaller and also requires less time for finding Eigen vectors. Medical image reconstruction is performed with increased values of 2DPCA and observed from results that reconstruction accuracy improves with increase of principal component values. Retrieval is performed for transformed image space by calculating the Euclidean Distance(ED) between 2DPCA values of unknown images with database images. Minimum distance classifier is used for retrieval, which is simple classifier. Simulation results are reported by considering different medical images and showed that simulation results provide increased retrieval accuracy. Further, Segmentation of retrieved medical images is obtained using k-means clustering algorithm.

scholarly journals Medical Image Retrieval Approach by Texture Features Fusion Based on Hausdorff Distance

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Sun Xiaoming ◽  
Zhang Ning ◽  
Wu Haibin ◽  
Yu Xiaoyang ◽  
Wu Xue ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Hausdorff Distance ◽  
Medical Image ◽  
Medical Information ◽  
Medical Images ◽  
Brain Mri ◽  
Texture Features ◽  
Image Feature ◽  
Medical Image Retrieval

Medical images play an important role in the hospital diagnosis and treatment, which include a lot of valuable medical information. Manually annotated viewing is obviously not effective in managing large amounts of medical imaging data. Hence it is an important task to establish an efficient and accurate medical image retrieval system. In this paper, a medical image retrieval approach based on Hausdorff distance combining Tamura texture features and wavelet transform algorithm is proposed. The combination of Tamura texture features and wavelet transform features can extract the texture features of medical images more effectively, and Hausdorff distance can reflect the overall similarity of medical image feature set. In this paper, 6 group experiments of brain MRI database and the lung CT database were conducted separately. Experiments show that the proposed approach has higher accuracy than a single feature texture algorithm and is also higher than the approach of Tamura texture features and wavelet transform features combined with Euclidean distance.

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 Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shaomin Zhang ◽  
Lijia Zhi ◽  
Tao Zhou
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Deep Convolutional Neural Network ◽  
Medical Image Retrieval ◽  
Mode Decomposition

Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the accuracy and speed of the search process. In this paper, we propose a deep convolutional neural network- (CNN-) based framework to learn concise feature vector for medical image retrieval. The medical images are decomposed into five components using empirical mode decomposition (EMD). The deep CNN is trained in a supervised way with multicomponent input, and the learned features are used to retrieve medical images. The IRMA dataset, containing 11,000 X-ray images, 116 classes, is used to validate the proposed method. We achieve a total IRMA error of 43.21 and a mean average precision of 0.86 for retrieval task and IRMA error of 68.48 and F1 measure of 0.66 on classification task, which is the best result compared with existing literature for this dataset.

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