Content based medical image retrieval using topic and location model

Shamna, P.; Govindan, V. K.; Nazeer, K. A. Abdul

doi:10.1016/j.jbi.2019.103112

Cited by 36 publications

(18 citation statements)

References 16 publications

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“…In the same discipline, Shamna et al [11] examined and analysed the automated medical image retrieval system, integrating the performance-enhancing subject and position probabilities. They presented an automated medical image retrieval system in their study using topic and location models.…”

Section: A Local Featuresmentioning

confidence: 99%

Implementing Relevance Feedback for Content-Based Medical Image Retrieval

Ahmed

2020

IEEE Access

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Content-based image medical retrieval (CBMIR) is a technique for retrieving medical images on the basis of automatically derived image features such as colour, texture and shape. There are many applications of CBMIR, such as teaching, research, diagnosis and electronic patient records. The retrieval performance of a CBMIR system depends mainly on the representation of image features, which researchers have studied extensively for decades. Although a number of methods and approaches have been suggested, it remains one of the most challenging problems in current (CBMIR) studies, largely due to the well-known ''semantic gap'' issue that exists between machine-captured low-level image features and human-perceived high-level semantic concepts. There have been many techniques proposed to bridge this gap. This study proposes a novel relevance feedback retrieval method (RFRM) for CBMIR. The feedback implemented here is based on voting values performed by each class in the image repository. Here, eighteen using colour moments and GLCM texture features were extracted to represent each image and eight common similarity coefficients were used as similarity measures. After briefly researching using a single random image query, the top images retrieved from each class are used as voters to select the most effective similarity coefficient that will be used for the final searching process. Our proposed method is implemented on the Kvasir dataset, which has 4,000 images divided into eight classes and was recently widely used for gastrointestinal disease detection. Intensive statistical analysis of the results shows that our proposed RFRM method has the best performance for enhancing both recall and precision when it uses any group of similarity coefficients. INDEX TERMS Content-based image retrieval, feature extraction, voting method, relevance feedback.

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Section: A Local Featuresmentioning

confidence: 99%

Implementing Relevance Feedback for Content-Based Medical Image Retrieval

Ahmed

2020

IEEE Access

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“…Here, using Deep Learning, these techniques will assess total efficiency with suggested scheme using CBMIR methods. In the latest Deep Convolution Neural Network (DeepCNN), we will compare [34]. The achievement of DeepCNN was evaluated with a comparable database where more than 23 different areas were the same as the database.…”

Section: Methodsmentioning

confidence: 99%

“…We begin each other from threshold distance (euclidean) with cluster centers to avoid, thus having the better words or phrases. It is based on the database and the number of subjects in each information set is chosen [30].…”

Section: Srinivas Publicationmentioning

confidence: 99%

Modified Location Model Estimation using Content Based Medical Image Retrieval

Kumar

2019

IJMTS

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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.

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“…The intensity difference at an image's local region is defined as contrast that is expressed in equation (4).…”

Section: • Contrastmentioning

confidence: 99%

An Efficient Medical Image Retrieval and Classification using Deep Neural Network

Chethan¹

2020

IJST

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Background/Objectives: The main objective of this work is to obtain an efficient brain tumor image retrieval and classification using Deep Neural Network (DNN). Methods/Statistical analysis: The features from the medical images are extracted by using tamura feature extraction, Local Ternary Pattern (LTP) and Histogram of Oriented Gradients (HOG). Subsequently, an Infinite Feature Selection (Inf-FS) technique is incorporated to select optimum features from feature vector, which leads to improve the classification process using sparse auto encoder based DNN. Furthermore, the retrieval performance of the proposed method is improved by Euclidean Distance technique. Findings: An Open Access Series of Imaging Studies (OASIS) and Contrast Enhanced- Magnetic Resonance Image (CE-MRI) datasets are utilized to analyze the proposed method. The sparse auto encoder based DNN classification scheme yields an overall accuracy of 95.34% in OASIS dataset and 99.87% in CEMRI dataset with improved sensitivity, specificity, error rate. The retrieval performance of proposed technique is assessed in terms of Average Retrieval Precision (ARP) and compared with two existing methods such as Local Mesh Vector Co-occurrence Pattern (LMVCoP) and Content Based Image Retrieval- Convolutional Neural Network (CBIR-CNN). The ARP of the proposed method for CE-MRI and OASIS dataset is 98.33% and 88.25% that is high when compared to the CBIR-CNN, LMVCoP method. Novelty/Applications: An appropriate feature selection using Inf-FS and DNN based nonlinear feature data classification are used in the applications of medical image retrieval.

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Content based medical image retrieval using topic and location model

Cited by 36 publications

References 16 publications

Implementing Relevance Feedback for Content-Based Medical Image Retrieval

Implementing Relevance Feedback for Content-Based Medical Image Retrieval

Modified Location Model Estimation using Content Based Medical Image Retrieval

An Efficient Medical Image Retrieval and Classification using Deep Neural Network

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