Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions
| Main Authors: | K., Savitha S., Naveen, N. C. |
|---|---|
| Format: | Article eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2019
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4063207 |
| ctrlnum |
4063207 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>K., Savitha S.</creator><creator>Naveen, N. C.</creator><date>2019-04-01</date><description>Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system.</description><identifier>https://zenodo.org/record/4063207</identifier><identifier>10.11591/ijece.v9i2.pp982-991</identifier><identifier>oai:zenodo.org:4063207</identifier><language>eng</language><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><source>International Journal of Electrical and Computer Engineering (IJECE) 9(2) 982-991</source><subject>Chest radiograph</subject><subject>Chest x-ray</subject><subject>Disease detection</subject><subject>Lung</subject><subject>Segmentation</subject><title>Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions</title><type>Journal:Article</type><type>Journal:Article</type><recordID>4063207</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Journal:eJournal |
| author |
K., Savitha S. Naveen, N. C. |
| title |
Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions |
| publishDate |
2019 |
| topic |
Chest radiograph Chest x-ray Disease detection Lung Segmentation |
| url |
https://zenodo.org/record/4063207 |
| contents |
Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system. |
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IOS17403.4063207 |
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Universitas PGRI Palembang |
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189 |
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library:university library |
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Perpustakaan Universitas PGRI Palembang |
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587 |
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Marga Life in South Sumatra in the Past: Puyang Concept Sacrificed and Demythosized |
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17403 |
| city |
KOTA PALEMBANG |
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SUMATERA SELATAN |
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IOS17403 |
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2022-07-26T02:42:42Z |
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