Title :

Neural Adaptive Fractional Order Differential based Algorithm for Medical Image Enhancement

Author(s) :

Krouma, Houda [Auteur]
Ferdi, Youcef [Auteur]
Taleb-Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]

Conference title :

2018 International Conference on Signal, Image, Vision and their Applications (SIVA )

City :

Guelma

Country :

Algérie

Start date of the conference :

2018-11-26

Publisher :

IEEE

English keyword(s) :

"image enhancement"
"fractional differential calculus"
"artificial neural network"

HAL domain(s) :

Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique

English abstract : [en]

In this paper, we propose an adaptive fractional differential calculus based technique for image enhancement. The adaptive fractional order used in the fractional differential mask is computed through a neural network based ...
Show more >In this paper, we propose an adaptive fractional differential calculus based technique for image enhancement. The adaptive fractional order used in the fractional differential mask is computed through a neural network based scheme. The training of the neural network is achieved by using adaptive fractional orders calculated by means of AFDA (Adaptive Fractional Differential Approach) algorithm for different medical images. After training, the neural network calculates the appropriate adaptive fractional order that will be substituted in the mask to enhance the image. We perform some experiments on medical images then compare the enhancing performance with that of the AFDA algorithm, demonstrating that the proposed method leads to a better quality of enhanced images, giving rise to clearer edges and richer texture with less computational complexityShow less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL