بخشبندی تصاویرCT کبد با پرتوشناسی بر مبنای الگوریتم آبپخشان
محورهای موضوعی : زیست شناسیمحسن آقاطاهری خوزانی 1 , فتانه تقی زاده فرهمند 2
1 - دانشجوی کارشناسی ارشد، گروه پرتوپزشکی، دانشکده علوم پایه، واحد قم، دانشگاه آزاد اسلامی، قم، ایران.
2 - دانشیار، گروه فیزیک، دانشکده علوم پایه، واحد قم، دانشگاه آزاد اسلامی، قم، ایران
کلید واژه: پرتوشناسی, تصاویرCT, پردازش تصویر, کبد, الگوریتم آبپخشان.,
چکیده مقاله :
هدف: هدف پژوهش حاضر بخشبندی تصاویر CT کبد با پرتوشناسی بر مبنای الگوریتم آبپخشان است. مواد و روش‌ها: در اﯾﻦ مطالعه یک روش ﻧﯿﻤﻪﺧﻮدﮐﺎر ﺑﺮای ﺑﺨﺶﺑﻨﺪی ﺗﻮﻣﻮرﻫﺎی ﮐﺒﺪ ﺑﺎ اﺳﺘﻔﺎده از ﺗﺼﺎوﯾﺮ ﺳﯽﺗﯽ اﺳﮑﻦ اراﺋﻪ ﺷﺪه اﺳﺖ. اﺑﺘﺪا ﺗﻮﺳﻂ ﮐﺎرﺑﺮ ﺑﺎﻓﺖ ﺗﻮﻣﻮر و ﮐﺒﺪ ﺑﺎ اﻧﺘﺨﺎب ﻧﻘﺎﻃﯽ ﺗﻌﯿﯿﻦ ﻣﯽﮔﺮدد. ﺳﭙﺲ ﺑﻪ ﮐﻤﮏ روش آﺑﭙﺨﺸﺎن، ﺷﮑﻞﺷﻨﺎﺳﯽ ﺳﻪ ﺑﻌﺪی ﻧﻘﺎط اوﻟﯿﻪ در ﺗﻮﻣﻮر و ﮐﺒﺪ ﺗﻌﯿﯿﻦ ﻣﯽﺷﻮﻧﺪ. ﺳﭙﺲ ﺑﺎ روش اﻧﺘﺸﺎر ﻗﯿﻮد واﺑﺴﺘﻪ، ﺗﺨﻤﯿﻦ ﺑﺮﭼﺴﺐﻫﺎی ﺑﺎﻓﺖ ﺗﻮﻣﻮر و ﮐﺒﺪ اﻧﺠﺎم ﻣﯽﺷﻮد. ﺑﺎ ﮔﺮﻓﺘﻦ اﺷﺘﺮاك ﺑﯿﻦ ﺑﺮﭼﺴﺐﻫﺎی ﺑﺪﺳﺖ آﻣﺪه، ﻣﺤﺪوده ﻗﺮار ﮔﺮﻓﺘﻦ ﻣﺮز ﺗﻮﻣﻮر ﺑﺪﺳﺖ ﻣﯽآﯾﺪ و ﻧﻬﺎﯾﺘﺎً ﺑﺎ اﺳﺘﻔﺎده از آﺷﮑﺎرﺳﺎز ﻟﺒﻪ ﮐﻨﯽ، ﻣﺮزﻫﺎی ﻧﻬﺎﯾﯽ ﺗﻮﻣﻮر ﻣﺸﺨﺺ ﻣﯽﺷﻮﻧﺪ. یافته‌ها: تغییرات تعداد نقاط اولیه، بر روی نتایج خروجی تاثیر کمی داشته است. در روش CAP با توجه به آنکه تخمین داده‌ها با استفاده از نقاط نمونه‌برداری شده صورت می‌گیرد و اطراف این نقاط تخمین زده میشود، با هر تعداد نمونه اولیه، روشCAP قادر است تا نتایج نهایی را تولید نماید، که این امر نشان‌دهنده قدرت بالای روش CAP در تخمین داده‌ها است. نتیجه‌گیری: بکارگیری الگوریتم آبپخشان باعث بهبود بخشبندی تصاویر CT کبد با پرتوشناسی میشود.
Purpose: The purpose of the present study is to segment the CT images of the liver with radiology based on the watershed algorithm. Materials and methods: In this study, a semi-automated method for dividing liver tumors using CT scan images has been presented. First, the tumor and liver tissue is determined by the user with point selection. Then, with the help of Abpakhshan method, the three-dimensional morphology of the primary points in the tumor and liver are determined. Then, estimation of tumor and liver tissue labels is done with the method of propagation of dependent constraints. By taking the distance between the obtained labels, the tumor boundary is obtained, and finally, the final boundaries of the tumor are determined by using the edge detector. Findings: Changes in the number of initial points have little effect on the output results. In the CAP method, considering that the data estimation is done using the sampled points and estimates around these points, with any number of initial samples, the CAP method is able to produce the final results, which shows the high power of the CAP method in It is an estimate of the data. Conclusion: The use of the watershed algorithm improves the segmentation of CT images of the liver with radiology.
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