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The Investigation of the Effects of Different Filters on Mammogram Images

Year 2018, Volume: 2 Issue: 1, 55 - 68, 30.03.2018
https://doi.org/10.31200/makuubd.346442

Abstract

Mammogram is a widespread imaging technique to early detect
breast cancer. It can detect micro scale calcium deposits (microcalcification) known as early signs of
breast cancer., computer-aided diagnosis
(CAD) systems are commonly used to detect
of microcalcifications on mammograms. The
first step of CAD system is cleaning noises on mammography images. In order to
clean or decrease noise on images,
several filters are used. The purpose of this study is denoising mammogram
images that include micro calcification with different filters and comparing of
filter results. For this, firstly 50 mammogram images are obtained from Digital
Database for Screening Mammography (DDSM). Microcalcification
located areas which stated in their data
file on mammograms are cropped at 512x512 pixels. Each image matrices are
filtered by median and moving average filter in spatial
domain as well as high pass and low pass filter in frequency domain. The filtered images are compared by means of mean
squared error (MSE) and peak signal-noise ratio (PSNR) after frequency domain
filters contrast adjustment. At the end of the study, the optimal filter will
be determined for cleaning mammograms without an effect on single or clustered microcalcification. 

References

  • Akbay, C. (2015). Applıcatıon Of Image Enhancement Algorıthms To Improve The Vısıbılıty And Classıfıcatıon Of Mıcrocalcıfıcatıons In Mammograms. MIDDLE EAST TECHNICAL UNIVERSITY.
  • Aslan Avdan, A. (2013). Duktal Karsinoma İn Situ’da BI-RADS Tanımlayıcıları İle Moleküler Prognostik Faktörler Arasındaki İlişki. GAZİ ÜNİVERSİTESİ TIP.
  • Fu, J. C., Lee, S. K., Wong, S. T. C., Yeh, J. Y., Wang, A. H., & Wu, H. K. (2005). Image segmentation feature selection and pattern classification for mammographic microcalcifications. Computerized Medical Imaging and Graphics, 29(6), 419–429. http://doi.org/10.1016/j.compmedimag.2005.03.002
  • Glasbey, C. A., & Horgan, G. W. (1995). Image Analysis for the Biological Sciences (1. edition). University of Michigan: Wiley.
  • Gonzalez, R. C., Woods, R. E., Telatar, Z., Tora, H., Arı, H., & Kalaycıoğlu, A. (2014). Sayısal Görüntü İşleme. Ankara: Palme Yayıncılık.
  • Heath, M., Bowyer, K., Kopans, D., Moore, R., & Kegelmeyer, W. P. (2001). The Digital Database for Screening Mammography. In M. J. Yaffe (Ed.), Proceedings of the Fifth International Workshop on Digital Mammography (pp. 212–218). Medical Physics Publishing.
  • Kim, J. K., & Park, H. W. (1999). Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Transactions on Medical Imaging, 18(3), 231–238. http://doi.org/10.1109/42.764896
  • Kim, J. K., Park, J. M., Song, K. S., & Park, H. W. (1997). Adaptive mammographic image enhancement using first derivative and local statistics. Medical Imaging, IEEE Transactions on, 16(5), 495–502. http://doi.org/10.1109/42.640739
  • Kumar, M., Thakkar, V. M., Bhadauria, H. S., Kumar, I., Pant, G. B., & College, E. (2016). Mammogram ’ s Denoising in Spatial and Frequency Domain, (October), 654–659.
  • Kurt, B., & Nabİyev, V. V. (2010). Dijital Mamografi Görüntülerinin Kontrast Sınırlı Adaptif Histogram Eşitleme ile İyileştirilmesi. In VII. Ulusal Tıp Bilişimi Kongresi (pp. 67–78).
  • Memiş, A. (2002). Meme Radyolojisi.
  • Murthy, R. K., Valero, V., & Buchholz, T. A. (2016). Breast Cancer. Clinical Radiation Oncology, 1284–1302.e3. http://doi.org/10.1016/B978-0-323-24098-7.00086-1
  • Nagaiah, K., Manjunathachari, K., & Rajinikanth, T. V. (2016). Advanced image enhancement method for mammogram analysis. 2016 International Conference on Recent Trends in Information Technology (ICRTIT), 1–5. http://doi.org/10.1109/ICRTIT.2016.7569554
  • Pak, F., Kanan, H. R., & Alikhassi, A. (2015). Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution. Computer Methods and Programs in Biomedicine, 122(2), 89–107. http://doi.org/10.1016/j.cmpb.2015.06.009
  • Qian, W., Sun, W., & Zheng, B. (2015). Improving the efficacy of mammography screening: the potential and challenge of developing new computer-aided detection approaches. Expert Review of Medical Devices, 12(5), 497–9. http://doi.org/10.1586/17434440.2015.1068115
  • Redman, A., Lowes, S., & Leaver, A. (2015). Imaging techniques in breast cancer. Surgery (United Kingdom), 34(1), 8–18.
  • Romualdo, L. C. D. S., Vieira, M. A. D. C., & Schiabel, H. (2009). Mammography images restoration by quantum noise reduction and inverse MTF filtering. Proceedings of SIBGRAPI 2009 - 22nd Brazilian Symposium on Computer Graphics and Image Processing, (1), 180–185. http://doi.org/10.1109/SIBGRAPI.2009.12
  • Shen, L., Rangayyan, R. M., & Desautels, J. E. L. (1994). Application of Shape-Analysis to Mammographic Calcifications. IEEE Transactions on Medical Imaging, 13(2), 263–274.
  • Singh, V., Rajpal, N., & Murthy, K. S. (2008). A Neuro Fuzzy Model for Image Compression in Wavelet Domain. In A. Elmoataz, O. Lezoray, F. Nouboud, & D. Mammass (Eds.), Image and Signal Processing: 3rd International Conference, ICISP 2008. Cherbourg-Octeville, France, July 1 - 3, 2008. Proceedings (pp. 46–58). Berlin, Heidelberg: Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-540-69905-7_6
  • Soltanian-Zadeh, H., Rafiee-Rad, F., & Siamak Pourabdollah-Nejad, D. (2004). Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms. Pattern Recognition, 37(10), 1973–1986. http://doi.org/10.1016/j.patcog.2003.03.001
  • Starck, J., & Murtagh, F. (2006). Handbook of Astronomical Data Analysis. Analysis, 338. http://doi.org/10.1007/978-3-540-33025-7
  • Veldkamp, W. J. H., & Karssemeijer, N. (2000). Normalization of local contrast in mammograms. IEEE Transactions on Medical Imaging, 19(7), 731–738. http://doi.org/10.1109/42.875197
  • Vijikala, V., Jyothi, V., & College, E. (n.d.). Identıfıcatıon of most preferentıal denoısıng method for mammogram.

Mamogram İmgeleri Üzerinde Farklı Süzgeçlerin Etkilerinin İncelenmesi

Year 2018, Volume: 2 Issue: 1, 55 - 68, 30.03.2018
https://doi.org/10.31200/makuubd.346442

Abstract

Mamaografi, meme kanserinin erken
teşhisi için kullanılan yaygın bir görüntüleme tekniğidir ve meme kanserinin
başlangıç aşaması olarak kabul edilen küçük kalsiyum birikintilerini (mikrokalsifikasyonlar)
görüntüleyebilme özelliğine sahiptir. Mikrokalsifikasyonların mamografi
üzerindeki tespitleri için bilgisayar destekli tespit (BDT) sistemleri sıklıkla
kullanılmaktadır. BDT sistemlerin ilk basamağı mamografi üzerinde oluşan
gürültüleri temizlemektir. Gürültü temizleme veya azaltma işlemi için çeşitli
süzgeçler kullanılmaktadır. Bu çalışmada mikrokalsifikasyon içeren mamografi
görüntülerin çeşitli süzgeçlerle temizlenmesi ve sonuçlarının karşılaştırılması
hedeflenmektedir. Bunun için öncelikle Digital Database for Screening
Mammography (DDSM) veritabanındaki mamografilerden mikrokalsifikasyon içeren 20
adet mamografi imgesi seçilmiştir. Alınan mamografilerden mikrokalsifikasyon
içeren kısımları vertabanında verilen koordnatlar ile 512x512 piksel boyutunda
kesilmiştir. Her bir görüntü matrisi uzamsal bölgede ortanca ve ortalama
süzgeçten, frekans bölgesinde ise alçak geçiren ve yüksek geçiren süzgeçlerden
geçirilerek kontrast ayarlanmış görüntü sonuçları ortalama hata karesi ve doruk
işaret-gürültü oranı ile  
karşılaştırılmıştır. Çalışma sonucunda tek mikrokalsifikasyonlara ve
mikrokalsifikasyon gruplarına etki etmeden, mamografilerde gürültü temizlemek
için en uygun süzgecinin hangisi olduğu tespit edilecektir. 

References

  • Akbay, C. (2015). Applıcatıon Of Image Enhancement Algorıthms To Improve The Vısıbılıty And Classıfıcatıon Of Mıcrocalcıfıcatıons In Mammograms. MIDDLE EAST TECHNICAL UNIVERSITY.
  • Aslan Avdan, A. (2013). Duktal Karsinoma İn Situ’da BI-RADS Tanımlayıcıları İle Moleküler Prognostik Faktörler Arasındaki İlişki. GAZİ ÜNİVERSİTESİ TIP.
  • Fu, J. C., Lee, S. K., Wong, S. T. C., Yeh, J. Y., Wang, A. H., & Wu, H. K. (2005). Image segmentation feature selection and pattern classification for mammographic microcalcifications. Computerized Medical Imaging and Graphics, 29(6), 419–429. http://doi.org/10.1016/j.compmedimag.2005.03.002
  • Glasbey, C. A., & Horgan, G. W. (1995). Image Analysis for the Biological Sciences (1. edition). University of Michigan: Wiley.
  • Gonzalez, R. C., Woods, R. E., Telatar, Z., Tora, H., Arı, H., & Kalaycıoğlu, A. (2014). Sayısal Görüntü İşleme. Ankara: Palme Yayıncılık.
  • Heath, M., Bowyer, K., Kopans, D., Moore, R., & Kegelmeyer, W. P. (2001). The Digital Database for Screening Mammography. In M. J. Yaffe (Ed.), Proceedings of the Fifth International Workshop on Digital Mammography (pp. 212–218). Medical Physics Publishing.
  • Kim, J. K., & Park, H. W. (1999). Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Transactions on Medical Imaging, 18(3), 231–238. http://doi.org/10.1109/42.764896
  • Kim, J. K., Park, J. M., Song, K. S., & Park, H. W. (1997). Adaptive mammographic image enhancement using first derivative and local statistics. Medical Imaging, IEEE Transactions on, 16(5), 495–502. http://doi.org/10.1109/42.640739
  • Kumar, M., Thakkar, V. M., Bhadauria, H. S., Kumar, I., Pant, G. B., & College, E. (2016). Mammogram ’ s Denoising in Spatial and Frequency Domain, (October), 654–659.
  • Kurt, B., & Nabİyev, V. V. (2010). Dijital Mamografi Görüntülerinin Kontrast Sınırlı Adaptif Histogram Eşitleme ile İyileştirilmesi. In VII. Ulusal Tıp Bilişimi Kongresi (pp. 67–78).
  • Memiş, A. (2002). Meme Radyolojisi.
  • Murthy, R. K., Valero, V., & Buchholz, T. A. (2016). Breast Cancer. Clinical Radiation Oncology, 1284–1302.e3. http://doi.org/10.1016/B978-0-323-24098-7.00086-1
  • Nagaiah, K., Manjunathachari, K., & Rajinikanth, T. V. (2016). Advanced image enhancement method for mammogram analysis. 2016 International Conference on Recent Trends in Information Technology (ICRTIT), 1–5. http://doi.org/10.1109/ICRTIT.2016.7569554
  • Pak, F., Kanan, H. R., & Alikhassi, A. (2015). Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution. Computer Methods and Programs in Biomedicine, 122(2), 89–107. http://doi.org/10.1016/j.cmpb.2015.06.009
  • Qian, W., Sun, W., & Zheng, B. (2015). Improving the efficacy of mammography screening: the potential and challenge of developing new computer-aided detection approaches. Expert Review of Medical Devices, 12(5), 497–9. http://doi.org/10.1586/17434440.2015.1068115
  • Redman, A., Lowes, S., & Leaver, A. (2015). Imaging techniques in breast cancer. Surgery (United Kingdom), 34(1), 8–18.
  • Romualdo, L. C. D. S., Vieira, M. A. D. C., & Schiabel, H. (2009). Mammography images restoration by quantum noise reduction and inverse MTF filtering. Proceedings of SIBGRAPI 2009 - 22nd Brazilian Symposium on Computer Graphics and Image Processing, (1), 180–185. http://doi.org/10.1109/SIBGRAPI.2009.12
  • Shen, L., Rangayyan, R. M., & Desautels, J. E. L. (1994). Application of Shape-Analysis to Mammographic Calcifications. IEEE Transactions on Medical Imaging, 13(2), 263–274.
  • Singh, V., Rajpal, N., & Murthy, K. S. (2008). A Neuro Fuzzy Model for Image Compression in Wavelet Domain. In A. Elmoataz, O. Lezoray, F. Nouboud, & D. Mammass (Eds.), Image and Signal Processing: 3rd International Conference, ICISP 2008. Cherbourg-Octeville, France, July 1 - 3, 2008. Proceedings (pp. 46–58). Berlin, Heidelberg: Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-540-69905-7_6
  • Soltanian-Zadeh, H., Rafiee-Rad, F., & Siamak Pourabdollah-Nejad, D. (2004). Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms. Pattern Recognition, 37(10), 1973–1986. http://doi.org/10.1016/j.patcog.2003.03.001
  • Starck, J., & Murtagh, F. (2006). Handbook of Astronomical Data Analysis. Analysis, 338. http://doi.org/10.1007/978-3-540-33025-7
  • Veldkamp, W. J. H., & Karssemeijer, N. (2000). Normalization of local contrast in mammograms. IEEE Transactions on Medical Imaging, 19(7), 731–738. http://doi.org/10.1109/42.875197
  • Vijikala, V., Jyothi, V., & College, E. (n.d.). Identıfıcatıon of most preferentıal denoısıng method for mammogram.
There are 23 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ayşe Aydın Yurdusev

Canan Oral

Mahmut Hekim

Publication Date March 30, 2018
Acceptance Date December 10, 2017
Published in Issue Year 2018 Volume: 2 Issue: 1

Cite

APA Aydın Yurdusev, A., Oral, C., & Hekim, M. (2018). The Investigation of the Effects of Different Filters on Mammogram Images. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, 2(1), 55-68. https://doi.org/10.31200/makuubd.346442


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