{"corpus_id":70101065,"paper_sha":"3a0621b3ecce08ff8b5fa2b67d9721285016726c","doi":"10.5120/IJCA2018917554","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":2884581321,"dblp_id":null,"acl_id":null,"title":"Curvelet based Rayleigh CLAHE Medical Image Enhancement","year":2018,"publication_date":"2018-07-16","venue":"International Journal of Computer Applications","journal":{"name":"International Journal of Computer Applications","pages":null,"volume":null},"journal_issn":null,"journal_title":null,"publication_types":[],"pubmed_pub_types":null,"s2_fields_of_study":["Medicine","Computer Science"],"reference_count":23,"citation_count":4,"influential_citation_count":0,"is_open_access":false,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":null,"s2_open_access_landing_url":null,"s2_open_access_license":null,"s2_open_access_status":null,"pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"Image enhancement is one of the main issues in digital image processing. Image enhancement is done to obtain a high quality image. This makes output image better than original image. Images that are obtained from medical imaging systems are of low quality. This may happen because available range of possible gray levels may not be utilized properly. Therefore images may suffer from underexposure and overexposure problems. A new algorithm has been proposed in this paper to enhance such medical images. A comparison of existing image enhancement techniques with the proposed technique based on different performance parameters is presented. Experimental results show that proposed technique is better than various existing techniques.","claims":[{"public_id":"cl_d29a9d32b4b4b442b8f1b08078eb441e","status":"active","text":"A new algorithm is proposed to enhance low-quality medical images affected by underexposure and overexposure.","confidence":0.97,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_d29a9d32b4b4b442b8f1b08078eb441e"},{"public_id":"cl_e6bfb63f645300878b5c0803ff91584d","status":"active","text":"Comparison across different performance parameters shows the proposed technique performs better than various existing image enhancement techniques.","confidence":0.96,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_e6bfb63f645300878b5c0803ff91584d"}],"concepts":[{"public_id":"co_2452b97b42dd7316f2db496747ed328f","status":"active","name":"existing image enhancement techniques","description":"Previously available methods for improving image quality that are used as comparators.","types":["baseline method"],"aliases":["various existing techniques"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_2452b97b42dd7316f2db496747ed328f"},{"public_id":"co_7b9ede62232df922e4ccf78edef688a4","status":"active","name":"performance parameters","description":"Quantitative measures used to compare image enhancement methods in the experiments.","types":["evaluation metric"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_7b9ede62232df922e4ccf78edef688a4"},{"public_id":"co_ac1f9623c59c6db3d34bac852f27fabe","status":"active","name":"medical images","description":"Images produced by medical imaging systems that are the target of enhancement in this work.","types":["image data"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_ac1f9623c59c6db3d34bac852f27fabe"},{"public_id":"co_edfc12105b857e48982d58b6a1b683bc","status":"active","name":"Curvelet based Rayleigh CLAHE Medical Image Enhancement","description":"A medical image enhancement algorithm that combines curvelet-based processing, Rayleigh transformation, and contrast-limited adaptive histogram equalization.","types":["method"],"aliases":["proposed technique","proposed algorithm"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_edfc12105b857e48982d58b6a1b683bc"},{"public_id":"co_f0d1912bca5161da94808289fb629419","status":"active","name":"underexposure and overexposure problems","description":"Image quality issues caused by improper use of the available gray-level range.","types":["image quality issue"],"aliases":["underexposure","overexposure"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_f0d1912bca5161da94808289fb629419"}],"external_ids":{"DOI":"10.5120/IJCA2018917554","ArXiv":null,"PubMed":null,"PubMedCentral":null,"MAG":2884581321,"DBLP":null,"ACL":null},"open_access":{"is_open_access":false,"pdf_url":null,"landing_url":"https://sah.borca.ai/papers/70101065","source":null,"pdf_url_source":null,"license":null,"reason":"pdf_url_not_indexed"},"reference_availability":{"status":"available","references_indexed":true,"full_text_available":false,"full_text_source":null,"count_basis":"semantic_scholar_metadata","extraction_status":"not_applicable","reason":null},"source":{"provider":"episteme2","base_corpus":"semantic_scholar_dump","freshness_mode":"unknown","basis":["semantic_scholar_metadata","postgres_metadata"],"limits":["paper metadata is based on indexed upstream scholarly datasets","claims and concepts are available only for extracted papers","absence of claims or concepts means no extracted graph data is available in this response"],"status":"available","degraded":false,"degraded_reasons":[],"diagnostics":{"status":"available","degraded":false,"degraded_reasons":[],"metadata_status":"available","graph_status":"available","abstract_status":"available"},"source_flags":1},"paper_id":633172,"paper_uid":"1f9fd8a1-56f6-42a1-9825-9a6ce8b424e4","canonical_identity":{"paper_id":633172,"paper_uid":"1f9fd8a1-56f6-42a1-9825-9a6ce8b424e4","identity_status":"available","lookup_basis":"semantic_scholar_external_id","compatibility_path":"corpus_id"},"url":"https://sah.borca.ai/papers/70101065"}