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Images compression for medical diagnosis using neural networks

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Lanzarini, Laura Cristina

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Material type: Article

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Summary: Images compression is a widely studied topic. Conventional situations offer variable compression ratios depending on the image in question and, in general, do not yield good results for images that are rich in tones. This work is an application o f i mages compression o f patient's computed tomographies using neural networks, which allows to carry out both compression and decompression of the images with a fixed ratio of 8:1 and a loss of 2%.

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Item type	Home library	Collection	Call number	URL	Status	Date due
Capítulo de libro	Biblioteca de la Facultad de Informática	Biblioteca digital	A0588 (Browse shelf(Opens below))	Link to resource	No corresponde

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Previous	A0585 EDU-RAEE : acercarse ecologicamente a la era digital. ¿cómo reducir la brecha digital colaborando con el medio ambiente?	A0586 Parallel recognition and classification of objects	A0587 Comparative analysis of the method of assignment by classes in GAVaPS	A0588 Images compression for medical diagnosis using neural networks	A0589 Model evolution and system evolution	A0590 A hierarchical triangulation for multiresolution terrain models	A0591 Performance of scientific processing in networks of workstations : matrix multiplication example	Next

Formato de archivo: PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)

Images compression is a widely studied topic. Conventional situations offer variable compression ratios depending on the image in question and, in general, do not yield good results for images that are rich in tones. This work is an application o f i mages compression o f patient's computed tomographies using neural networks, which allows to carry out both compression and decompression of the images with a fixed ratio of 8:1 and a loss of 2%.

Journal of Computer Science & Technology; 1(2)