Automated and effective content-based mammogram retrieval using wavelet based CS-LBP feature and self-organizing map

Singh, Vibhav Prakash; Srivastava, Rajeev

doi:10.1016/j.bbe.2017.09.003

Cited by 22 publications

(10 citation statements)

References 20 publications

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“…Assume D h as the referenced pixel manifested with red in RG, the variation direction's angle is given by β, and the distance among the referenced pixel and its adjacent pixels in the direction β is given by C. To demonstrate, the distance "C = 1 is colored with yellow, C = 2 is colored with green, and C = 3 is colored with blue." At the referenced pixel D h , the direction value of a vector is denoted in Equation (9).…”

Section: Local Vector Patternmentioning

confidence: 99%

Optimal weighted hybrid pattern for content based medical image retrieval using modified spider monkey optimization

Darapureddy

Karatapu²,

Battula

2020

Int J Imaging Syst Tech

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The current approaches for image retrieval are more concentrating on numerous image features. Texture, shape, spatial information, and color are the fundamental features to deal with flexible image datasets. This paper aims to develop new Content-Based Image Retrieval System based on Optimal Weighted Hybrid Pattern. Two relevant patters like Local Vector Pattern and Local Derivative Pattern are intended to develop a novel Content-Based Image Retrieval system. The optimal weighted hybrid pattern is implemented to derive a new feature vector, so that the weight is optimized by a modified optimization algorithm called Improved Local Leader-based Spider Monkey Optimization to maximize the precision and recall of the retrieved images. The retrieval of the image is done by measuring the similarity based on Mean Square Distance between the features of query image as well as training image. Finally, the performance comparison of the proposed and the traditional patterns shows its reliable performance. K E Y W O R D S content-based medical image retrieval, improved local leader-based spider monkey optimization, local derivative pattern, local vector pattern, machine learning-based classification, novel descriptors, optimal weighted hybrid pattern

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Section: Local Vector Patternmentioning

confidence: 99%

Optimal weighted hybrid pattern for content based medical image retrieval using modified spider monkey optimization

Darapureddy

Karatapu²,

Battula

2020

Int J Imaging Syst Tech

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“…Singh et al [30] have proposed an automated and effective content based mammogram retrieval on the basis of two approaches including SOM and wavelet features based CS-LBP. It is very fast, effective and automatic.…”

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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“…On the other case when sudden grey scale variation at the borders of neighbouring box leads to box under counting situation. In this framework a modified DBC approach is proposed and is described in next section [6][7][8]. To address this challenge and reduce experts' overhead, we propose automatic mass region extraction using the maximum entropy principle and shape feature (Circularity).…”

Section: Introductionmentioning

confidence: 99%

Modified Differential Box Counting in Breast Masses for Bioinformatics Applications

Devi¹,

Vidivelli²

2022

Computers, Materials &Amp; Continua

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Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer. The present research work is useful in image processing for characterizing shape and gray-scale complexity. The proposed Modified Differential Box Counting (MDBC) extract Fractal features such as Fractal Dimension (FD), Lacunarity, and Succolarity for shape characterization. In traditional DBC method, the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels. The problem is overcome by the proposed MDBC method that uses box over counting and under counting that covers the whole image with required scale. In MDBC method, the suitable box size selection and Under Counting Shifting rule computation handles over counting problem. An advantage of the model is that the proposed MDBC work with recently developed methods showed that our method outperforms automatic detection and classification. The extracted features are fed to K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) categorizes the mammograms into normal, benign, and malignant. The method is tested on mini MIAS datasets yields good results with improved accuracy of 93%, whereas the existing FD, GLCM, Texture and Shape feature method has 91% accuracy.

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Automated and effective content-based mammogram retrieval using wavelet based CS-LBP feature and self-organizing map

Cited by 22 publications

References 20 publications

Optimal weighted hybrid pattern for content based medical image retrieval using modified spider monkey optimization

Optimal weighted hybrid pattern for content based medical image retrieval using modified spider monkey optimization

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

Modified Differential Box Counting in Breast Masses for Bioinformatics Applications

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