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CCAST“2002年全国强场激光物理”研讨会

作者： a.talebpour M.Abdel-Fattah S.L.Chin Centre’ Optique Photonique et Laser （COPL） and Dept de Physique Universit Laual Qubec Qc. Canada GIK 7P4 Centre’ Optique Photonique et Laser （COPL） and Dept de Physique Universit Laual Qubec Qc. Canada GIK 7P4 Centre’ Optique Photonique et Laser （COPL） and Dept de Physique Universit Laual Qubec Qc. Canada GIK 7P4

＜正＞ As a result of focusing ultrafast Ti:Sapphire laser pulses in a gas. a plasma column is created. Its density does not exceed some limiting value as a result of the defocusing by the electrons resulting from muluphoton ionization and the involvement of high order nonlinearities. The spectrum of the radiation from the excited species in the column has little contribution from the plasma continuum and the line broadening is less than that of the spectra radiated from a plasma generated by a long laser pulse. Thus. the spectroscopic source obtained using short laser pulses is an excellent source for spectroscopy, （?）2000 Published by Elsevier Science B.V.

关键词： Propagation: Ti:Sapphire laser pulse Plasma: Spectroscopy Photo-emission spectrum

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Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system

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The Journal of Biomedical Research 2017年第5期31卷 419-427页

作者： Mahdi Alizadeh Omid Haji Maghsoudi Kaveh Sharzehi Hamid Reza Hemati Alireza Kamali Asl Alireza.talebpour Department of Bioengineering Temple University Philadelphia PAl9121 USA Department of Medicine Section of Gastroenterology School of Medicine Temple University Philadelphia PA 19140. USA Department of Radiation Medicine Engineering ShahidBeheshti University Tehran *** Iran. Department of Electrical and Computer Engineering ShahidBeheshti University Tehran *** Iran.

Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more *** purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification. Primarily, 32 features incorporating four statistical measures(contrast, correlation, homogeneity and energy) calculated from co-occurrence metrics were computed. Then, mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features. Finally, a trained classifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthy and unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.

关键词： adaptive neuro-fuzzy inference system co-occurrence matrix wireless capsule endoscopy texture feature

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