Texture-based medical image retrieval in compressed domain using compressive sensing
Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical im...
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| Veröffentlicht in: | International journal of bioinformatics research and applications Jg. 10; H. 2; S. 129 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Switzerland
2014
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| ISSN: | 1744-5485 |
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| Abstract | Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality. |
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| AbstractList | Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality. Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality.Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality. |
| Author | Yadav, Kuldeep Srivastava, Avi Mittal, Ankush Ansari, M A |
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| CitedBy_id | crossref_primary_10_1080_21681163_2017_1344933 crossref_primary_10_1007_s11760_016_0996_0 crossref_primary_10_1080_21681163_2016_1193447 crossref_primary_10_1080_09720502_2020_1723923 crossref_primary_10_1016_j_jestch_2016_05_006 |
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| Keywords | medical images basis pursuit algorithm compressed domain image retrieval discrete cosine transform image quality DCT texture based image retrieval acquisition speed compressive sampling content based image retrieval compressive sensing medical imaging |
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| SubjectTerms | Algorithms Data Compression - methods Databases, Factual Diagnostic Imaging - instrumentation Diagnostic Imaging - methods Humans Image Enhancement Information Storage and Retrieval Pattern Recognition, Automated - methods Radiography, Thoracic - methods Software |
| Title | Texture-based medical image retrieval in compressed domain using compressive sensing |
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