摘要(英) |
Liver diseases are always on the list of the top 10 causes of death in Taiwan. Early primary liver cancer is difficult to detect because the initial symptoms are usually not obvious. But unless it is discovered when the tumor is very small, liver cancer is difficult to control. therefore, we desire to build a numerical simulation of the liver structure, including blood vessel topography, liver surface. Before the simulation, we should segment liver from MR images.
Medical images mostly contain complicated structures, and image segmentation is a key task in many medical applications. Their precise segmentation is necessary for simulation. Since seeking the subject for scanning MRI isn′t a simple matter, we use a mouse liver image to do simulation. However, mouse liver boundaries in MR images are usually unclear, the traditional edge-based method for segmentation is unsuitable. In this paper, we propose a way that creating a new image is combined T1-weighted (T1), T2-weighted MRI (T2) and T1-weighted MRI with contrast enhancement (T1 C+(Primovist)) image. We compare the image which doing confusion component removing with the original image after segmentation using k-means method afterward. The result presents that accuracy is improved. In the future, we look forward to applying on the numerical simulation.
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參考文獻 |
[1] L.N.Vu,J.N.Morelli,andJ.Szklaruk.BasicMRIfortheliveroncologistsand surgeons. Journal ofHepatocellularCarcinoma, 5:37,2017.
[2] 13 -liverandgallbladder.InP.M.TreutingandS.M.Dintzis,editors, Comparative AnatomyandHistology, pages193 201.AcademicPress,2012.
[3] W. BurgerandM.J.Burge.Regionsinbinaryimages.In Digital ImageProcessing, pages 209–252.Springer,2016.
[4] D. L.Pham,C.Xu,andJ.L.Prince.Currentmethodsinmedicalimageseg enta-tion. AnnualReviewofBiomedicalEngineering, 2(1):315–337,2000.
[5] GodfreyNHounsfield.Computerizedtransverseaxialscanning(tomography):Part 1. descriptionofsystem. The Britishjournalofradiology, 46(552):1016–1022,1973.
[6] A. Kumar,D.Welti,andR.R.Ernst.ImagingofmacroscopicobjectsbyN R fourier zeugmatography. Naturwissenschaften, 62(1):34,1975.
[7] L. Dora,S.Agrawal,R.Panda,andA.Abraham.State-of-the-artmethodsforbrain tissue segmentation:Areview. IEEE ReviewsinBiomedicalEngineering, 10:235–249,
2017.
[8] N. A.Mohamed,M.N.Ahmed,andA.Farag.Modifiedfuzzyc-meaninmedical
image segmentation.In Proceedingsofthe20thAnnualInternationalConferenceof
the IEEE EngineeringinMedicineandBiologySociety.Vol.20BiomedicalEng-ineeringTowardstheYear2000andBeyond(Cat.No.98CH36286), volume3,pages
1377–1380. IEEE,1998.
[9] S. Yazdani,R.Yusof,A.Karimian,M.Pashna,andA.Hematian.Imagesegmenta-
tion methodsandapplicationsinMRIbrainimages. IETE TechnicalReview, 32(6):
413–427, 2015.
[10] B. Fischl,D.H.Salat,E.Busa,M.Albert,M.Dieterich,C.Haselgrove,A.Van
Der Kouwe,R.Killiany,D.Kennedy,S.Klaveness,etal.Wholebrainsegmentation:
automated labelingofneuroanatomicalstructuresinthehumanbrain. Neuron, 33(3):
341–355, 2002. |