A new approach for effective retrieval and indexing of medical images

Aggarwal, Ashutosh; Sharma, Suchita; Singh, Karamjeet; Singh, Harpreet; Kumar, Sunil

doi:10.1016/j.bspc.2019.01.009

Cited by 15 publications

(2 citation statements)

References 42 publications

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“…Local Neighborhood Intensity Pattern (LNIP) is presented in [10] for pattern calculation. A new method based on orthogonal Fourier-Mellin moments (OFMMs) is proposed in [11] for effective indexing and retrieval of medical images. The analysis for MapReduce efficiency using parallel K-means algorithm for document clustering is proposed in [12].…”

Section: B Related Workmentioning

confidence: 99%

An Efficient Clustering Based CBIR System using Hadoop to Analyze Massive MRI Images Dataset for Early Disease Diagnosis

Singh,

Mann

2019

IJEAT

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In past decade, the use of medical imaging for disease diagnosis is increased rapidly. The medical images provide useful information about the anatomy of patients. The medical images are used not only to assist the doctors for diagnosis purpose, but also used in Research & Development for deeper insights and better understanding into cause and cure of numerous diseases. To retrieve medical images from large scale repositories in real time, there is urgent need of an efficient medical image retrieval system. For this purpose, an efficient clustering based content based image retrieval (CBIR) system using Hadoop is proposed to analyze massive magnetic resonance imaging (MRI) images dataset for early disease diagnosis. The proposed CBIR system uses Hadoop platform, local mesh peak valley edge patterns (LMePVEP) for feature extraction, MapReduce based parallel k-means algorithm for clustering and euclidean distance to measure similarity. The method proposed is tested and compared with state-of-the-art CBIR methods on massive MRI images dataset. The experimental results obtained show that the method proposed in our work outperforms traditional CBIR methods in terms of average retrieval time and mean average precision for massive MRI images dataset

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Section: B Related Workmentioning

confidence: 99%

An Efficient Clustering Based CBIR System using Hadoop to Analyze Massive MRI Images Dataset for Early Disease Diagnosis

Singh,

Mann

2019

IJEAT

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“…A segunda técnica para associar características aos objetos complexos é chamada "recuperação por conteúdo", como, por exemplo, em "Recuperação de Imagens por Conteúdo" (CBIR na sigla em inglês -Content-Based Image Retrieval) (AGGARWAL et al, 2019;BRAN-CATI;CAMASTRA, 2016;WELTER et al, 2012). Ela corresponde à associação de atributos obtidos a partir do próprio elemento complexo, extraídos por algoritmos de processamento automáticos chamados "vetores de características".…”

Section: Consultas Por Similaridade Em Conjuntos De Dados Complexosunclassified

Visualização de distribuições de dados complexos provenientes de fontes heterogêneas

Martins

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Este trabalho é dedicado aos meus pais, Silvia de F. M. Martins e José N. Martins que, desde muito antes da conclusão deste mestrado, se dedicaram e abdicaram de si mesmos em muitos momentos para me oferecerem um caminho cheio de oportunidades. Ao meu namorado, noivo e agora, marido, Dr. Hendrik Marques Soares, que me apoiou de todas as formas possíveis nos mais de mil dias de dedicação à pesquisa. Ao melhor cientista e ser humano que eu poderia ter tido como orientador, Prof. Dr. Caetano Traina Júnior, excepcional em sua capacidade de ouvir, ensinar e de representar o verdadeiro espírito da ciência.Por fim, para minha querida avó, Sylvia Marino (in memoriam), que foi exemplo de força e determinação.Obrigada, eu não teria conseguido sem vocês. RESUMOMARTINS, N. F. Visualização de distribuições de dados complexos provenientes de fontes heterogêneas. 2023. 83 p. Dissertação (Mestrado em Ciências -

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Medical Image Retrieval Using Efficient Texture and Color Patterns with Neural Network Classifier

Kumar

Sathiamoorthy

2020

Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019)

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A new approach for effective retrieval and indexing of medical images

Cited by 15 publications

References 42 publications

An Efficient Clustering Based CBIR System using Hadoop to Analyze Massive MRI Images Dataset for Early Disease Diagnosis

An Efficient Clustering Based CBIR System using Hadoop to Analyze Massive MRI Images Dataset for Early Disease Diagnosis

Visualização de distribuições de dados complexos provenientes de fontes heterogêneas

Medical Image Retrieval Using Efficient Texture and Color Patterns with Neural Network Classifier

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