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A M EDICAL I MAGE R EGISTRATION S YSTEM By Rahul Mourya Anurag Maurya Supervisor Dr. Rajeev Srivastava.

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Presentation on theme: "A M EDICAL I MAGE R EGISTRATION S YSTEM By Rahul Mourya Anurag Maurya Supervisor Dr. Rajeev Srivastava."— Presentation transcript:

1 A M EDICAL I MAGE R EGISTRATION S YSTEM By Rahul Mourya Anurag Maurya Supervisor Dr. Rajeev Srivastava

2 D EFINITION The term ‘ registration’ means determining the spatial alignment between images of the same or different subjects, acquired with the same or different modalities, and also the registration of images with the coordinate system of a treatment device or tracked localizer.

3 A PPLICATIONS Monitoring of healing therapy and tumor evolution Combination of sensors recording the anatomical body structure like MRI, CT with sensors monitoring functional and metabolic activities like PET,SPECT. Comparison of patient’s image with digital anatomical atlases, specimen classification Characterizing normal versus abnormal anatomical shape variations

4 C HALLENGES Transformations between images can vary widely and be highly non-rigid in nature Images acquired from different modalities may differ significantly in appearance and resolution Some data may be missing so that one-to-one correspondence is not available Each imaging modality introduces its own unique challenges, making it difficult to develop a single generic algorithm

5 G ENERAL P ROCEDURE : S TEPS

6 G ENERAL P ROCEDURE : S TEPS ( CONTD ….) FEATURE DETECTION Salient and distinctive objects are manually or automatically detected. For further processing, these features are represented by Control Points FEATURE MATCHING Correspondence the features detected in the sensed image and those detected in reference image is established

7 G ENERAL P ROCEDURE : S TEPS ( CONTD ….) TRANSFORM MODEL ESTIMATION The types and parameters of mapping functions aligning the sensed image with the reference image are computed by means of the established feature correspondence RESAMPLING AND TRANSFORMATION The sensed image is transformed by means of the mapping functions..

8 D ESIGN AND A NALYSIS

9 D ESIGN C ONSIDERATIONS Choose the approach : feature -based or intensity- based Choice of appropriate geometric transform model Whether to and how to explicitly model intensity changes Choose an error metric that incorporates the previous three choices Choose a minimization technique for minimizing the error metric yielding the desired transformation

10 O UR C HOICES An intensity based approach so as to avoid pitfalls involved in feature selection A transformation with a local affine model and a global smoothness constraint Intensity variations are modeled with local changes in brightness and contrast A standard MSE error is employed on intensity values An error function, linear in model parameters is augmented with a non linear smoothness constraint

11 H OW WE IMPLEMENTED ?

12 P LATFORM The software is implemented in MATLAB 7.8.0. The GUI is designed with MATLAB GUIDE.

13 O UR A PPROACH The problem of image registration between a source and a target image is formulated within a differential (non-feature based) framework A Gaussian pyramid is first built for both source an target images. The source and target images at the coarsest scale are then registered to obtain an initial estimation of registration map This initial estimate is used to warp the source image at next scale The warped source is the registered with corresponding target image

14 O UR A PPROACH The process is repeated at each level of pyramid Within each scale, the registration map is determined in an iterative fashion During each of these iterations, successive intermediate registration maps are accumulated to form a single map This approach improves the accuracy of registration Within each scale and each iteration, a smooth registration map is obtained

15 O UR A PPROACH Given a source and target image, an estimate of the registration map without smoothness is first obtained This initial estimate is used in the nonlinear iterative estimation of a smooth registration map These iterations are called smoothness iterations All these steps together form a complete algorithm

16 R EGISTRATION W ITH P ARTIAL D ATA o Source or target image has only partial data o In this case the registration algorithm fail.

17 EM A LGORITHM Example: Given sets of n data points that comes from two lines of form y(i) = a 1 x(i)+b 1 +n(i) ;y(i) = a 2 x(i)+b 2 +n(i) Problem: To estimate each model M1, M2 and determine to which model each data point belongs to. Simultaneously estimate the perimeters as well as segment the data.

18 EM A LGORITHM ( CONTD …) EM Algorithm: A two step iterative procedure Step1: The probability of each point q(i) belonging to model k is estimated as weight w k (i) Step2: The model parameter are updated using a weighted least-squares estimate These two steps are iterated until convergence of the estimated parameter.

19 EM A LGORITHM ( CONTD …) 1. Initialization: Assign values for the fixed constants σ 1 2, σ 2 2. Assign initial estimates for the model parameters a 1, a 2, b 1, b 2. 2. E­-Step: Compute the weights: This corresponds to the segmentation step. 3. M­-Step: Use the weights w 1 (i) and w 2 (i) to estimate the least­-squares solutions for a k, b k : This corresponds to the estimation step. Steps 1 and 2 are repeated until convergence of the estimated parameters.

20 R ESULTS Snap Shots

21 Example of Image registration(Rotation +Missing Data ).

22 Estimated Parameters

23 Example of Image Registration in Partial Data

24 Estimated Parameters

25 C ONCLUSION

26 C ONCLUSION ( CONTD …) Intensity-based image registration algorithm is a successful algorithm and it is helpful in avoiding various pitfalls concerned with feature-based approach due to difficulties involved with feature selection and matching Along with implementation of affine transformation and brightness and contrast factors, implementation of smoothness constraints has led to large improvement in final results.

27 W EAKNESSES

28 If the target image is rotated by a large angle from the source image then the results of registration are very poor. In this case, distortion is too large and cannot be detected even at coarsest level If the intensity variation between the source and target image is large and non-smooth then there is high distortion in brightness or contrast maps and hence algorithm fails

29 F UTURE S COPE

30 The algorithm can be modified and generalized so that it can also be used for registration of 3D volumes It can be modified to handle images with large rotations and high intensity variations.

31 R EFERENCES References 1) Senthil Periaswamy, Hany Farid, Medical Image Registration with Partial Data, Radiology Source, Volume 10, Issue 3, Pages 452-464 (June 2006) 2) Barbara Zitova, Jan Flusser, Image Registraton Method: a survey (June 2003)

32 T HANK Y OU


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