Content-based medical image retrieval by spatial matching of visual words

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

doi:10.1016/j.jksuci.2018.10.002

Cited by 24 publications

(22 citation statements)

References 29 publications

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“…With the increasing usage of bag-of-visual-words (BoVW) techniques, Shamna et al [12] proposed an unsupervised content-based medical image retrieval (CBMIR) framework based on visual word spatial matching. Additionally, Zhang et al [13] proposed a BoVW retrieval method for identifying discriminative characteristics between different medical images with a pruned dictionary based on the description of the latent semantic topic.…”

Section: B Visual Words Dictionarymentioning

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: B Visual Words Dictionarymentioning

confidence: 99%

Implementing Relevance Feedback for Content-Based Medical Image Retrieval

Ahmed

2020

IEEE Access

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“…The feature vector (F) is generated by using the features from the tamura, LTP and HOG descriptor. The following equation (9) represents the generated feature vector F = { f (1) , f (2) , f (3) , f (4) , f (5)…”

Section: Hog Descriptormentioning

confidence: 99%

“…The appropriate feature selection is used for constructing the low dimensional feature vectors to reduce the time consumption and to increase classification accurateness. The set of features given to the feature selection is F = { f (1) , f (2) , . .…”

Section: Infinite Feature Selectionmentioning

confidence: 99%

“…With the rapid advancement in medical imaging and automatic diagnosis systems, plenty of medical images are being collected, stored in DICOM (Digital Imaging and Communications in Medicine) format (1) . Many medical imaging devices such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and X-ray are used to acquire medical images.…”

Section: Introductionmentioning

confidence: 99%

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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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“…In [25] authors have proposed unsupervised CBMIR scheme using visual words spatial matching. Here spatial similarity of visual words is matched based on the Skip Similarity Index (SSI).…”

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 by spatial matching of visual words

Cited by 24 publications

References 29 publications

Implementing Relevance Feedback for Content-Based Medical Image Retrieval

Implementing Relevance Feedback for Content-Based Medical Image Retrieval

An Efficient Medical Image Retrieval and Classification using Deep Neural Network

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

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