Content based medical image retrieval using dictionary learning

Srinivas, M.B.; Naidu, R. Ramu; Sastry, C. S.; Mohan, C. Krishna

doi:10.1016/j.neucom.2015.05.036

Cited by 78 publications

(34 citation statements)

References 33 publications

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“…Finally, the fused image is reconstructed by fused sparse representation coefficients. For instance, PET/CT/MRI images are fused using the K-SVD-based learning dictionary and Orthogonal Matching Pursuit (OMP) algorithm [139]. Similarly, Sparse representation-based methods have been applied for fusions of CT/MRI and MR images [138,140,141].…”

Section: Sparse Representation and Compressive Sensing Based Methodsmentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.

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Section: Sparse Representation and Compressive Sensing Based Methodsmentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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“…As large portions of medical images in the dataset lack labels and annotations, semi-supervised and unsupervised techniques are required in the retrieval systems. Uunsupervised image retrieval based on the clustering method using K-SVD executes iterations between grouping similar images into clusters and generating a dictionary for clusters until clusters converge [42]. The advantage of this method is that it requires no training data for classification and is not restricted to a specific context.…”

Section: Image Retrievalmentioning

confidence: 99%

Radiological images and machine learning: Trends, perspectives, and prospects

Zhang

Sejdić

2019

Computers in Biology and Medicine

156

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The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computeraided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.

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“…Error rate during similarity computation is large and it takes large computation time. K-SVD algorithm is proposed, which uses clustering method for dictionary learning for similarity analysis on group of large medical image collections [33]. A user query image is matched using orthogonal matching unit (OMP) algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Biswas*,

Roy,

Biswas

2019

IJEAT

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 Abstract: 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.

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Content based medical image retrieval using dictionary learning

Cited by 78 publications

References 33 publications

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Radiological images and machine learning: Trends, perspectives, and prospects

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

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