A comparative evaluation of 3 different free-form deformable image registration and contour propagation methods for head and neck MRI : the case of parotid changes radiotherapy

Purpose: To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approachesthe commercial MIM, the open-source Elastix software, and an optimized version of it. Materials and Methods: Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness. Results: A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods. Conclusion: The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations

Multi Modal Medical Image Registration: A New Data Driven Approach

Image registration is a challenging task in building computer-based diagnostic systems. One type of image modality will not be able to provide all information needed for better diagnostic. Hence data from multiple sources/image modalities should be combined. In this work canonical correlation analysis (CCA) based image registration approach has been proposed. CCA provides the framework to integrate information from multiple sources. In this work, the information contained in both images is used for image registration task. T1-weighted, T2- weighted and FLAIR MRI images has Multimodal registration done on it. The algorithm provided better results when compared with mutual information based image registration approach. The work has been carried out using the 3D rigid registration of CT and MRI images. The work is carried out using the public datasets, and later performance is evaluated with the work carried out by Research scholars previously. Our algorithm performs better with mutual information based image registration. Medical image registration of multimodality images like MRI, MRI-CT, and MRI-CT-PET. In this paper for MRI-CT Medical Image Registration CT image is used as a fixed image and MRI image as moving image and later compared results with some benchmark algorithm presented in literature such as correlation coefficient, correlation ratio, and mutual information and normalized mutual information methods

Local Mismatch Location and Spatial Scale Detection in Image Registration

Image registration is now a well understood problem and several techniques using a combination of cost functions, transformation models and optimizers have been reported in medical imaging literature. Parametric methods often rely on the efficient placement of control points in the images, that is, depending on the location and scale at which images are mismatched. Poor choice of parameterization results in deformations not being modeled accurately or over parameterization, where control points may lie in homogeneous regions with low sensitivity to cost. This lowers computational efficiency due to the high complexity of the search space and might also provide transformations that are not physically meaningful, and possibly folded. Adaptive methods that parameterize based on mismatch in images have been proposed. In such methods, the cost measure must be normalized, heuristics such as how many points to pick, resolution of the grids, choosing gradient thresholds and when to refine scale would have to be ascertained in addition to the limitation of working only at a few discrete scales. In this paper we identify mismatch by searching the entire image and a wide range of smooth spatial scales. The mismatch vector, containing location and scale of mismatch is computed from peaks in the local joint entropy. Results show that this method can be used to quickly and effectively locate mismatched regions in images where control points can be placed in preference to other regions speeding up registration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85931/1/Fessler223.pd

Registration of Ultrasound Images Using an Information-Theoretic Feature Detector

In this paper, we present a new method for ultrasound image registration. For each image to be registered, our method first applies an ultrasound-specific information-theoretic feature detector, which is based on statistical modeling of speckle and provides a feature image that robustly delineates important edges in the image. These feature images are then registered using differential equations, the solution of which provides a locally optimal transformation that brings the images into alignment. We describe our method and present experimental results demonstrating its effectiveness, particularly for low contrast, speckled images. Furthermore, we compare our method to standard gradient-based techniques, which we show are more susceptible to misregistration

Registration of Ultrasound Images Using an Information-Theoretic Feature Detector

In this paper, we present a new method for ultrasound image registration. For each image to be registered, our method first applies an ultrasound-specific information-theoretic feature detector, which is based on statistical modeling of speckle and provides a feature image that robustly delineates important edges in the image. These feature images are then registered using differential equations, the solution of which provides a locally optimal transformation that brings the images into alignment. We describe our method and present experimental results demonstrating its effectiveness, particularly for low contrast, speckled images. Furthermore, we compare our method to standard gradient-based techniques, which we show are more susceptible to misregistration

Rinnan lämpökuvien aikasarjojen stabilointi

Dynamic infrared imaging (DIRI) is an emerging technology for the early detection of breast cancer. In this method time-series of thermal breast images are obtained. The patient motion in the time-series can distort the DIRI analysis in such a way that the detection of breast cancer becomes impossible. Image registration can be used to eliminate the patient motion from the time-series data. In this thesis, two different registration algorithms were tested: Thirion's demons algorithm and an algorithm based on an affine transformation. Furthermore, a combined method where the affine method is used as a pre-registration step for the demons method was tested. The algorithms were implemented with Matlab and their performance in the task of registering a time-series of thermal breast images was evaluated using four different performance metrics. The registration algorithms were implemented for time-series data of 20 healthy (no malignant lesions) subjects. The demons method outperformed the affine method and is recommended as a suitable tool for time-series registration of thermal breast images. The combined method achieved slightly improved results compared to the demons method but with significantly increased computation time.Dynaaminen lämpökuvantaminen on lupaava menetelmä rintasyövän aikaiseen havaitsemiseen. Menetelmässä rinnoista otetaan lämpökuvien aikasarja. Kuvantamisen aikana tapahtuva potilaan liike voi vaikeuttaa aikasarjan analysointia niin, että rintasyövän tunnistaminen ei ole mahdollista. Liike voidaan poistaa aikasarjasta kuvastabiloinnin avulla. Tässä työssä tutkittiin kahta kuvastabilointiin kehitettyä algoritmia: Thirionin demons-algoritmia ja algoritmia, joka perustuu affiiniin muunnokseen. Lisäksi tutkittiin yhdistettyä menetelmää, jossa affiinia menetelmää käytetään esiaskeleena demons-menetelmälle. Algoritmien laskenta toteutettiin Matlabilla. Algoritmien tuottaman tuloksen laatua arvioitiin neljällä erillisellä laatumittarilla. Testidatana käytettiin aikasarjoja, jotka oli kuvattu 20:stä terveestä (ei pahanlaatuisia kasvaimia) potilaasta. Demons-menetelmä osoittautui affiinia menetelmää paremmaksi. Demons-menetelmää voidaan suositella rintojen lämpökuvien aikasarjojen stabilointiin. Yhdistetty menetelmä tuotti hiukan parempia tuloksia kuin demons-menetelmä, mutta vaati huomattavasti enemmän laskenta-aikaa

3D nonrigid medical image registration using a new information theoretic measure.

International audienceThis work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy