You are here

Home » Content

SAFRA KESESİ GÖRÜNTÜLERİNİN AYRIK DALGACIK DÖNÜŞÜMÜ VE DURAĞAN DALGACIK DÖNÜŞÜMÜ KULLANILARAK KARŞILAŞTIRILMALI ANALİZİ

ANALYSIS OF GALL-BLADDER IMAGES BY USING STATIONARY WAVELET TRANSFORM AND DISCRETE WAVELET TRANSFORM

Journal Name:

  • Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi

Publication Year:

  • 2008

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
The edge detection algorithms are important in biomedical image analysis. In this work histogram equalization, the discrete wavelet transform and the stationary wavelet transform techniques were used to improve the quality of the gall bladder ultrasonic images for edge detection. Also the median filtering algorithm was used after applying the both techniques. Then the performances of these algorithms were compared by several performance measures such as image entropy, paired t-test, and CPU time.
Bookmark/Search this post with
Abstract (Original Language): 
Ayrıt sezimi algoritmaları biyomedikal görüntü analizinde önemli algoritmalardır. Bu çalışmada ayrıt sezimi için, histogram eşleme, ayrık dalgacık dönüşümü (ADD) ve durağan dalgacık dönüşümü (DDD) yöntemleri, safra kesesi ses üstü imgelerinin kalitesini iyileştirmede kullanılmıştır. Ayrıca, ortanca süzgeçleme algoritması, bu tekniklerden sonra görüntü üzerine uygulanmıştır. Sonuçta bu algoritmaların başarımı, görüntü entropi, parçalı t-testi ve CPU zamanı gibi çeşitli başarım ölçütleri kulllanılarak karşılaştırılmıştır.
FULL TEXT (PDF): 
PDF icon arastirmax-safra-kesesi-goruntulerinin-ayrik-dalgacik-donusumu-duragan-dalgacik-donusumu-kullanilarak-karsilastirilmali-analizi.pdf
  • 2
23-30
  • Turkish

REFERENCES

References: 

Czerwinski R. N., Jones D. L., O‟Brien W. D. (1999): “Detection of Lines and Boundaries in Speckle Images-Application to Medical Ultrasound”, IEEE Trans. Medical Imaging, Cilt 18, No. 2, s. 126-136.
Chalana V., Kim Y. (1997): “A Methodology for Evaluation of Boundary Detection Algorithms on Medical Images”, IEEE Trans. Medical Imaging, Cilt 16, No. 5, s. 642-652.
Xie J., Jiang Y., Tsui H. T. (2005): “Segmentation of Kidney from Ultrasound Images Based on Texture and Shape Priors”, IEEE Trans. Medical Imaging, Cilt 24, No. 1, sf 45-57.
Pizer S. M. vd. (1987): “Adaptive Histogram Equalization and Its Variations”, Computer Vision, Graphics, and Image Processing, Cilt 39, s. 355-368.
Lehr L., Capek P. (1985): “Histogram Equalization of CT Images”, Radiology, Cilt 154, s. 163-169.
Coifman R., Wickerhauser M. V. (1994): “Wavelets and Adapted Waveform Analysis”, Wavelets: Mathematics and Applications, CRC Press.
Daubechies I. (1992): “Ten Lectures on Wavelets”, SIAM Publisher.
Gonzales R. C., Woods R. E. (1992): “Digital Image Processing”, Addison Wesley Pub. Company.
Mallat S. (1989): “A Theory for Multiscale Decomposition: The Wavelet Representation”, IEEE Trans. on Pattern analysis and Machine Intelligence, Cilt 11, No. 7, s. 674-693.
Aarnink R. G., Pathak S. D., Rosette J. J. M. C. H., Debruyne F. M. J., Kim Y., Wijkstra H. (1998): “Edge Detection in Prostatic Ultrasound Images Using Integrated Edge Maps”, Ultrasonics, Cilt 36, No. 1-5, s. 635-642.

Thank you for copying data from http://www.arastirmax.com