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Yağlı bir karaciğerin ultrasonografik görüntüleri üzerinde histogram bazlı kantitatif derecelendirme

Yıl 2020, Cilt: 8 Sayı: 2, 306 - 319, 28.06.2020

Ömer Karal Sevil Toprak

https://doi.org/10.29109/gujsc.646817

Öz

Karaciğerde
anormal bir yağ birikmesi karaciğer hücrelerine zarar verebilir ve karaciğer
hastalıklarına neden olabilir. Yağ birikimi erken evrelerde teşhis edilirse;
yağlı karaciğer ile ilişkili fonksiyonel anormallikler ortaya çıkarılabilir ve
derecesi belirlenebilir. Yağlı karaciğeri teşhis etmek ve karaciğerdeki yağ
derecesini belirlemek için çeşitli tıbbi görüntüleme teknikleri kullanılır. Bu
görüntüleme tekniklerinden en sık kullanılanı invaziv olmayan, uygun maliyetli
ve kolay erişilebilir olan ultrason ile görüntülemedir. Ultrason, karaciğer
yağlanmasının tespitinde oldukça yaygın kullanılmasına rağmen, karaciğerdeki
yağ seviyesini ultrason görüntülerinden belirlemek için bir uzman görüşüne
ihtiyaç duyulmaktadır. Ayrıca aynı karaciğer görüntüsündeki yağ seviyesi,
farklı zamanlarda değerlendirildiğinde aynı veya başka bir uzman tarafından
farklı şekillerde yorumlanabilir. Bu nedenle, tanı özneldir ve uzmanın bilgi ve
tecrübesine bağlı olarak değişebilir. Bu çalışmada, nesnelliği arttırmak ve
uzmana yardımcı olmak amacıyla, ultrason görüntülerinden yağlı karaciğeri
belirlemek ve derecelendirmek için Ağırlıklandırılmış Histogram [Weighted
Histogram (WH)] adı verilen yeni bir nicel ölçüm yöntemi önerilmiştir. Ayrıca,
önerilen yöntemin kullanım kolaylığını arttırmak için MATLAB ile bir Grafiksel
Kullanıcı Arayüz (Graphical User Interfac - GUI) tasarlanmıştır. Önerilen
yöntem yalnızca klinik değerlendirmedeki sübjektif farklılıkların neden olduğu
yanlış teşhisi azaltmakla kalmayacak, aynı zamanda erken tanı ile yağlı
karaciğer ve yağlanmanın derecelendirilmesinin kantitatif olarak belirlenmesini
de sağlayacaktır.

Anahtar Kelimeler

Karaciğer yağlanması Karaciğer yağlanma seviyesi Ultrason ile görüntüleme Ağırlıklandırılmış histogram MATLAB arayüzü

Teşekkür

Bu çalışmanın gerçekleştirilmesinde değerli bilgilerini bizlerle paylaşan Uzm. Dr. Radyolog Ender Evcik’e teşekkür ederiz.

Kaynakça

  • [1] Çolak, Y., Tuncer, İ. Nonalkolik karaciğer yağlanması ve steatohepatit. İstanbul Üniversitesi Cerrahpaşa Tıp Fakültesi Sürekli Eğitim Tıp Etkinlikleri, 58(91-98), (2017).
  • [2] Kleiner, D. E., Brunt, E. M., Van Natta, M., Behling, C., Contos, M. J., Cummings, O. W., et al. Design and validation of a histological scoring system. Hepatology, 41(1313-1321), (2005).
  • [3] Sonsuz, A., Baysal, B. Karaciğer yağlanması ve non alkolik steatohepatit. Güncel Gastroenteloji, 15(98-106), (2011).
  • [4] Gaggini, M., Morelli, M., Buzzigoli, E., Defronzo, R., Bugianesi, E., Gastaldelli, A. Non-Alcoholic fatty liver disease (NAFLD) and its connection with insulin resistance, dyslipidemia, atherosclerosis and coronary heart disease. Nutrients, 5(1544–1560), (2013).
  • [5] İçer, S., Coşkun, A., İkizceli, T. Quantitative grading using grey relational analysis on ultrasonographic images of a fatty liver. Journal of Medical Systems, 36(2521-2528), (2012).
  • [6] Chen, H. Y., Wang, J. R., Lu, K. Y. The evaluation of liver function via grey relational analysis. IEEE Int. Conf. on Sys., Man and Cybernetics, (783-786), (2009).
  • [7] Zeng, Q. L., Li, D. D., Yang, Y. B. VIKOR method with enhanced accuracy for multiple criteria decision making in healthcare management. Journal of Medical Systems, 37(9908), (2013).
  • [8] İmamoğlu, F. G., İmamoğlu, Ç., Çiledağ, N., Arda, K., Tola, M. Düzgöl, C. Classification of hepatosteatosis with ultrasonography and analysis of the effect of hepatosteatosis degree on the liver function tests. Medical Journal of Muğla Sıtkı Koçman University, 2(23-28), (2015).
  • [9] Acharya, U.R., Fujita, H., Sudarshan, V.K., Mookiah, M.R.K., Koh, E.W.J., Tan, J.H., et al. An integrated index for identification of fatty liver disease using radon transform and discrete cosine transform features in ultrasound images. Information Fusion, 31(43-53), (2016).
  • [10] Kodama, Y., Ng, C. S., Wu, T. T., Ayers, G. D., Curley, S. A., Abdalla, E. K. et al. Comparison of CT methods for determining the fat content of the liver. American Journal of Roentgenology, 188(1307-1312), (2007).
  • [11] Singh, M., Singh, S., Gupta, S. An information fusion based method for liver classification using texture analysis of ultrasound images. Inf Fusion, 19(91-6), (2013).
  • [12] İdilman, İ. S., Karçaaltıncaba, M. Karaciğer yağlanması tanısında ve yağlanma miktarının belirlenmesinde radyolojik tanı yöntemleri, Güncel Gastroenteloji, 18(112-118), (2014).
  • [13] Lupsor, M. P., Stefanescu, H., Mureșan, D., Florea, M., Erzsebet, M. S., Maniu, A., et al. Noninvasive assessment of liver steatosis using ultrasound method. Med. Ultrasound, 16(236-245), (2014).
  • [14] Strauss, S., Gavish, E., Gottlieb, P., Katsnelson, L. Interobserver and intraobserver variability in the sonographic assessment of fatty liver, American Journal of Roentgenology, 189(320-323), (2007).
  • [15] Yoneda, M., Suzuki, K., Kato, K., Fujita, K., Nozaki, Y., Hosono, K., et al. Nonalcoholic fatty liver disease: US-based acoustic radiation force impulse elastography. Radiology, 256(640-647), (2010).
  • [16] Saadeh, S., Younossi, Z. M., Remer, E. M., Gramlich, T., Ong, J. P., Hurley, M., Mullen, K. D., Cooper, J. N., Sheridan, M. J. The utility of radiological imaging in nonalcoholic fatty liver disease. Gastroenterology, 123(745-50), (2002).
  • [17] Dandıl, E. Bilgisayarlı Tomografi Görüntüleri Üzerinde Karaciğer Bölgesinin Bilgisayar Destekli Otomatik Bölütleme Uygulaması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 3(712-728), (2019).
  • [18] Bharti, P., Mittal, D., Ananthasivan, R. Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review. Ultrasonic Imaging, 39(33-61), (2017).
  • [19] Virmani, J., Kumar, V., Kalra, N., Khandelwal, N. SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors. Society for imaging informatics in medicine, 26(530-543), (2012).
  • [20] Owjimer, M., Danyali, H., Helfroush, M. S. An improved method for liver diseases detection by ultrasound image analysis. Journal of Medical Signals Sensors, 5(21-9), (2015).
  • [21] Santos, J., Silva, J. S., Santos, A. A., Soares, P. B. Detection of pathologic liver using ultrasound images. Biomedical Signal Processing and Control, 14(248-255), (2014).
  • [22] Mukherjee, S., Chakravorty, A., Ghosh, K., Roy, M., Adhikari, A., Mazumdar, S., Corroborating the subjective classification of ultrasound images of normal and fatty human livers by the radiologist through texture analysis and SOM. IEEE 15th International Conference on Advanced Computing and Communications, (197-202), (2007).
  • [23] Andrade, A., Silva, J.S., Santos, J., Belo-Soares, P., Classifier approaches for liver steatosis using ultrasound images. Procedia Technol, 5(763–70), (2012).
  • [24] İçer, S., Coşkun, A., İkizceli, T. Quantitative grading using grey relational analysis on ultrasonographic images of a fatty liver. Journal of Medical Systems, 36(2521-2528), (2012).
  • [25] Subramanya, M., Kumar, V., Mukherjee, S., Saini, M. A CAD system for B-mode fatty liver ultrasound images using texture features. Journal of Medical Engineering & Technology, 39(123-130), (2015).
  • [26] Kaur, K. Digital image processing in ultrasound images. International Journal on Recent and Innovation Trends in Computing and Communication, 1(388-393), (2013).
  • [27] Shetti, P. P., Patil, A. P. Performance comparison of mean, median and wiener filter in MRI image de-noising. International Journal for Research Trends and Innovation, 2(371-375), (2017).
  • [28] MATLAB, 27 Şubat 2019, https://ch.mathworks.com/help/images/ref/wiener2.html.
  • [29] Gonzalez, R. C., Woods, R. E. (2009). Digital Image Processing (Third edition). Pearson, International Edition.
  • [30] Lin, Y., H., Lee, P. C., Chang, T. P. Practical expert diagnosis model based on the grey relational analysis technique. Expert Systems with Applications, 36(1523–1528), (2009).
  • [31] Chang, C. L., Tsai, C. H., Chen, L., Applying grey relational analysis to the decathlon evaluation model, International Journal of The Computer. The Internet and Management, 11(54–62), (2003).
  • [32] Senger, Ö., Albayrak, Ö. K., A Study on performance appraisal by gray incidence analysis. International Journal of Economic & Administrative Studies, 17(235-258), (2016).
  • [33] Deng, L. Introduction to grey system theory. The Journal of Grey System, 1(1–24), (1989).
  • [34] Chen, H. Y., Wang, J. R., Lu, K. Y. The evaluation of liver function via grey relational analysis, IEEE Int. Conf. on Sys., Man and Cybernetics. 783-786, 2009.
  • [35] Xu, W., Hou, Y., Ye, Z. A fast image match method based on water wave optimization and gray relational analysis. 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems, (771-776), (2017).
  • [36] Slavek, N., Jovic, A. Application of grey system theory to software projects ranking. Automatika, 53(284-293), (2012).

Histogram-based quantitative grading on ultrasonographic images of a fatty liver

Yıl 2020, Cilt: 8 Sayı: 2, 306 - 319, 28.06.2020

Ömer Karal Sevil Toprak

https://doi.org/10.29109/gujsc.646817

Öz

An
abnormal fat accumulation in the liver can damage the liver cells and cause
liver diseases. If the fat accumulation in the liver is diagnosed in the early
stages; the functional abnormalities associated with fatty liver can be
revealed and the severity of its can be assessed. There are several medical
imaging techniques to diagnose fatty liver and determine the grade of fat in
the liver. One of these techniques is the ultrasound imaging, which is
non-invasive, cost-effective and easily accessible. However, there is always a
need for an expert opinion to determine the level of fat in the liver from
ultrasound images. Furthermore, the level of fat in the same liver image may be
interpreted differently by the same or another expert when evaluated at
different times. In order to increase objectivity and assist the expert, in
this paper, a new quantitative measurement method called the Weighted Histogram
is proposed to determine and grade the fatty liver from ultrasound images. In
addition, the Graphical User Interface (GUI) is designed to grade the fatty
liver with MATLAB to improve ease of use. The proposed methodology will not
only reduce false diagnosis caused by subjective differences in clinical
assessment, but also quantitative assessment of fatty liver and grade by early
diagnosis.

Anahtar Kelimeler

Fatty liver Liver fat level Ultrasound image processing Weighted Histogram MATLAB GUI

Kaynakça

  • [1] Çolak, Y., Tuncer, İ. Nonalkolik karaciğer yağlanması ve steatohepatit. İstanbul Üniversitesi Cerrahpaşa Tıp Fakültesi Sürekli Eğitim Tıp Etkinlikleri, 58(91-98), (2017).
  • [2] Kleiner, D. E., Brunt, E. M., Van Natta, M., Behling, C., Contos, M. J., Cummings, O. W., et al. Design and validation of a histological scoring system. Hepatology, 41(1313-1321), (2005).
  • [3] Sonsuz, A., Baysal, B. Karaciğer yağlanması ve non alkolik steatohepatit. Güncel Gastroenteloji, 15(98-106), (2011).
  • [4] Gaggini, M., Morelli, M., Buzzigoli, E., Defronzo, R., Bugianesi, E., Gastaldelli, A. Non-Alcoholic fatty liver disease (NAFLD) and its connection with insulin resistance, dyslipidemia, atherosclerosis and coronary heart disease. Nutrients, 5(1544–1560), (2013).
  • [5] İçer, S., Coşkun, A., İkizceli, T. Quantitative grading using grey relational analysis on ultrasonographic images of a fatty liver. Journal of Medical Systems, 36(2521-2528), (2012).
  • [6] Chen, H. Y., Wang, J. R., Lu, K. Y. The evaluation of liver function via grey relational analysis. IEEE Int. Conf. on Sys., Man and Cybernetics, (783-786), (2009).
  • [7] Zeng, Q. L., Li, D. D., Yang, Y. B. VIKOR method with enhanced accuracy for multiple criteria decision making in healthcare management. Journal of Medical Systems, 37(9908), (2013).
  • [8] İmamoğlu, F. G., İmamoğlu, Ç., Çiledağ, N., Arda, K., Tola, M. Düzgöl, C. Classification of hepatosteatosis with ultrasonography and analysis of the effect of hepatosteatosis degree on the liver function tests. Medical Journal of Muğla Sıtkı Koçman University, 2(23-28), (2015).
  • [9] Acharya, U.R., Fujita, H., Sudarshan, V.K., Mookiah, M.R.K., Koh, E.W.J., Tan, J.H., et al. An integrated index for identification of fatty liver disease using radon transform and discrete cosine transform features in ultrasound images. Information Fusion, 31(43-53), (2016).
  • [10] Kodama, Y., Ng, C. S., Wu, T. T., Ayers, G. D., Curley, S. A., Abdalla, E. K. et al. Comparison of CT methods for determining the fat content of the liver. American Journal of Roentgenology, 188(1307-1312), (2007).
  • [11] Singh, M., Singh, S., Gupta, S. An information fusion based method for liver classification using texture analysis of ultrasound images. Inf Fusion, 19(91-6), (2013).
  • [12] İdilman, İ. S., Karçaaltıncaba, M. Karaciğer yağlanması tanısında ve yağlanma miktarının belirlenmesinde radyolojik tanı yöntemleri, Güncel Gastroenteloji, 18(112-118), (2014).
  • [13] Lupsor, M. P., Stefanescu, H., Mureșan, D., Florea, M., Erzsebet, M. S., Maniu, A., et al. Noninvasive assessment of liver steatosis using ultrasound method. Med. Ultrasound, 16(236-245), (2014).
  • [14] Strauss, S., Gavish, E., Gottlieb, P., Katsnelson, L. Interobserver and intraobserver variability in the sonographic assessment of fatty liver, American Journal of Roentgenology, 189(320-323), (2007).
  • [15] Yoneda, M., Suzuki, K., Kato, K., Fujita, K., Nozaki, Y., Hosono, K., et al. Nonalcoholic fatty liver disease: US-based acoustic radiation force impulse elastography. Radiology, 256(640-647), (2010).
  • [16] Saadeh, S., Younossi, Z. M., Remer, E. M., Gramlich, T., Ong, J. P., Hurley, M., Mullen, K. D., Cooper, J. N., Sheridan, M. J. The utility of radiological imaging in nonalcoholic fatty liver disease. Gastroenterology, 123(745-50), (2002).
  • [17] Dandıl, E. Bilgisayarlı Tomografi Görüntüleri Üzerinde Karaciğer Bölgesinin Bilgisayar Destekli Otomatik Bölütleme Uygulaması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 3(712-728), (2019).
  • [18] Bharti, P., Mittal, D., Ananthasivan, R. Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review. Ultrasonic Imaging, 39(33-61), (2017).
  • [19] Virmani, J., Kumar, V., Kalra, N., Khandelwal, N. SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors. Society for imaging informatics in medicine, 26(530-543), (2012).
  • [20] Owjimer, M., Danyali, H., Helfroush, M. S. An improved method for liver diseases detection by ultrasound image analysis. Journal of Medical Signals Sensors, 5(21-9), (2015).
  • [21] Santos, J., Silva, J. S., Santos, A. A., Soares, P. B. Detection of pathologic liver using ultrasound images. Biomedical Signal Processing and Control, 14(248-255), (2014).
  • [22] Mukherjee, S., Chakravorty, A., Ghosh, K., Roy, M., Adhikari, A., Mazumdar, S., Corroborating the subjective classification of ultrasound images of normal and fatty human livers by the radiologist through texture analysis and SOM. IEEE 15th International Conference on Advanced Computing and Communications, (197-202), (2007).
  • [23] Andrade, A., Silva, J.S., Santos, J., Belo-Soares, P., Classifier approaches for liver steatosis using ultrasound images. Procedia Technol, 5(763–70), (2012).
  • [24] İçer, S., Coşkun, A., İkizceli, T. Quantitative grading using grey relational analysis on ultrasonographic images of a fatty liver. Journal of Medical Systems, 36(2521-2528), (2012).
  • [25] Subramanya, M., Kumar, V., Mukherjee, S., Saini, M. A CAD system for B-mode fatty liver ultrasound images using texture features. Journal of Medical Engineering & Technology, 39(123-130), (2015).
  • [26] Kaur, K. Digital image processing in ultrasound images. International Journal on Recent and Innovation Trends in Computing and Communication, 1(388-393), (2013).
  • [27] Shetti, P. P., Patil, A. P. Performance comparison of mean, median and wiener filter in MRI image de-noising. International Journal for Research Trends and Innovation, 2(371-375), (2017).
  • [28] MATLAB, 27 Şubat 2019, https://ch.mathworks.com/help/images/ref/wiener2.html.
  • [29] Gonzalez, R. C., Woods, R. E. (2009). Digital Image Processing (Third edition). Pearson, International Edition.
  • [30] Lin, Y., H., Lee, P. C., Chang, T. P. Practical expert diagnosis model based on the grey relational analysis technique. Expert Systems with Applications, 36(1523–1528), (2009).
  • [31] Chang, C. L., Tsai, C. H., Chen, L., Applying grey relational analysis to the decathlon evaluation model, International Journal of The Computer. The Internet and Management, 11(54–62), (2003).
  • [32] Senger, Ö., Albayrak, Ö. K., A Study on performance appraisal by gray incidence analysis. International Journal of Economic & Administrative Studies, 17(235-258), (2016).
  • [33] Deng, L. Introduction to grey system theory. The Journal of Grey System, 1(1–24), (1989).
  • [34] Chen, H. Y., Wang, J. R., Lu, K. Y. The evaluation of liver function via grey relational analysis, IEEE Int. Conf. on Sys., Man and Cybernetics. 783-786, 2009.
  • [35] Xu, W., Hou, Y., Ye, Z. A fast image match method based on water wave optimization and gray relational analysis. 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems, (771-776), (2017).
  • [36] Slavek, N., Jovic, A. Application of grey system theory to software projects ranking. Automatika, 53(284-293), (2012).
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Tasarım ve Teknoloji
Yazarlar

Ömer Karal 0000-0001-8742-8189

Sevil Toprak 0000-0002-3677-6439

Yayımlanma Tarihi 28 Haziran 2020
Gönderilme Tarihi 14 Kasım 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 8 Sayı: 2

Kaynak Göster

APA Karal, Ö., & Toprak, S. (2020). Yağlı bir karaciğerin ultrasonografik görüntüleri üzerinde histogram bazlı kantitatif derecelendirme. Gazi University Journal of Science Part C: Design and Technology, 8(2), 306-319. https://doi.org/10.29109/gujsc.646817
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