نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی پزشکی، گروه مهندسی پزشکی، دانشکده فنی و مهندسی، دانشگاه شاهد، تهران، ایران

2 استادیار، گروه مهندسی پزشکی، دانشکده فنی و مهندسی، دانشگاه شاهد، تهران، ایران

3 استاد، دانشکده علوم انفورماتیک و مهندسی، دانشگاه ریتسومیکان، شیگا، ژاپن

10.22041/ijbme.2014.13287

چکیده

مدل­های شکل آماری، از اطلاعات آماری جهت تفسیر و بررسی شکل استفاده می­کنند. اطلاعات آماری شامل میانگین و واریانس نقاط متناظر شکل­های مجموعه آموزش است. یافتن نقاط متناظر دربین نقاط اعضای مجموعه­ی آموزش، یکی­از چالش­های مهم در ساخت مدل شکل آماری است. درین مقاله، از روش CPDجهت یافتن تناظر بین نقاط استفاده شد. درین روش، با ترکیب تناظر فازی، الگوریتم سرد شدن معین و انطباق غیرصلب دو شکل، تناظر بین نقاط به دست آمد. پس­از یافتن نقاط متناظر، مدل شکل آماری با یک تبدیل صلب ایجاد شد. ارزیابی روش پیشنهادی با استفاده­از میزان فشردگی، قابلیّت تعمیم و اختصاصی بودن  انجام شد. مدل ساخته شده به کمک روش پیشنهادی با مدل­های ساخته شده به روش­های TPS-RPM، ICP ،MDL   مقایسه شد. نتایج نشان می­دهد که مدل پیشنهادی در معیار اختصاصی بودن با مقدار 06/0±21/0 مانند روش MDL عمل می­کند. در مورد معیارهای فشردگی و قابلیت تعمیم، نتایج به دست آمده با روش MDL مشابهت دارد. زمان متوسط اجرای الگوریتم در روش پیشنهادی 68 ثانیه است، در صورتی­که برای الگوریتم TPS-RPM390 ثانیه و برای الگوریتم MDL 3600 ثانیه است که برتری روش پیشنهادی را از نظر سرعت نشان می­دهد. هم­چنین در روش پیشنهادی، نسبت به روش­های ICP وTPS-RPM عملکرد بهتری به دست آمد.
 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Improvement of Statistical Shape Models for Non-rigid Tissues Using Coherent Point Drift Algorithm

نویسندگان [English]

  • Mehdi Delavari 1
  • Amir Hosein Foruzan 2
  • Ben Vi Chen 3

1 MS Student, Biomedical Engineering Department, Engineering Faculty, Shahed University, Tehran, Iran

2 Assistant Professor, Biomedical Engineering Department, Engineering Faculty, Shahed University, Tehran, Iran

3 Professor, College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan

چکیده [English]

Statistical Shape Models are used to interpret shapes. They include mean and variance of corresponding points of training shapes. One of the most important challenges in building statistical shape models is to establish correct correspondences among landmarks in a training set.  In this paper, the non-rigid CPD (Coherent Point Drift) method is used to find correct correspondences among points. This method uses both Deterministic Annealing and a non-rigid scheme to register two shapes simultaneously. Then, the statistical shape model is built using a rigid transformation. The proposed method is evaluated using Compactness, Generalization ability and Specificity measures. The built model is compared to models created using the ICP (Iterative Closest Point), TPS-RPM (Thin Plate Spline – Robust Point Matching) and MDL (Minimum Descreption Length) methods by these metrics. The results show that the proposed method performs like the MDL regarding Specificity measure (0.21±0.06). The Compactness and Generalization ability measures of the proposed method are very similar to those for the MDL method. The run-time of our proposed method is about 68 seconds which is faster than non-rigid TPS-RPM and MDL approaches (390 and 3600 seconds respectively). Our results are superior to the ICP and TPS-RPM algorithms.

کلیدواژه‌ها [English]

  • Coherent Point Drift
  • Corresponding points
  • Liver shape model
  • Medical image registration
  • Statistical shape models

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