Comparison of similarity measures for the task of template matching of masses on serial mammograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134879/1/mp1892.pd

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

Improvements in the registration of multimodal medical imaging : application to intensity inhomogeneity and partial volume corrections

Alignment or registration of medical images has a relevant role on clinical diagnostic and treatment decisions as well as in research settings. With the advent of new technologies for multimodal imaging, robust registration of functional and anatomical information is still a challenge, particular in small-animal imaging given the lesser structural content of certain anatomical parts, such as the brain, than in humans. Besides, patient-dependent and acquisition artefacts affecting the images information content further complicate registration, as is the case of intensity inhomogeneities (IIH) showing in MRI and the partial volume effect (PVE) attached to PET imaging. Reference methods exist for accurate image registration but their performance is severely deteriorated in situations involving little images Overlap. While several approaches to IIH and PVE correction exist these methods still do not guarantee or rely on robust registration. This Thesis focuses on overcoming current limitations af registration to enable novel IIH and PVE correction methods.El registre d'imatges mèdiques té un paper rellevant en les decisions de diagnòstic i tractament clíniques així com en la recerca. Amb el desenvolupament de noves tecnologies d'imatge multimodal, el registre robust d'informació funcional i anatòmica és encara avui un repte, en particular, en imatge de petit animal amb un menor contingut estructural que en humans de certes parts anatòmiques com el cervell. A més, els artefactes induïts pel propi pacient i per la tècnica d'adquisició que afecten el contingut d'informació de les imatges complica encara més el procés de registre. És el cas de les inhomogeneïtats d'intensitat (IIH) que apareixen a les RM i de l'efecte de volum parcial (PVE) característic en PET. Tot i que existeixen mètodes de referència pel registre acurat d'imatges la seva eficàcia es veu greument minvada en casos de poc solapament entre les imatges. De la mateixa manera, també existeixen mètodes per la correcció d'IIH i de PVE però que no garanteixen o que requereixen un registre robust. Aquesta tesi es centra en superar aquestes limitacions sobre el registre per habilitar nous mètodes per la correcció d'IIH i de PVE

Registering Histological and MR Images of Prostate for Image-based Cancer Detection

Rationale and Objectives Needle biopsy is currently the only way to confirm prostate cancer. To increase prostate cancer diagnostic rate, needles are expected to be deployed at suspicious cancer locations. High contrast MR imaging provides a powerful tool for detecting suspicious cancerous tissues. To do this, MR appearances of cancerous tissue should be characterized and learned from a sufficient number of prostate MR images with known cancer information. However, ground-truth cancer information is only available in histological images. Therefore, it is necessary to warp ground-truth cancerous regions in histological images to MR images by a registration procedure. The objective of this paper is to develop a registration technique for aligning histological and MR images of the same prostate. Material and Methods Five pairs of histological and T2-weighted MR images of radical prostatectomy specimens are collected. For each pair, registration is guided by two sets of correspondences that can be reliably established on prostate boundaries and internal salient blob-like structures of histological and MR images. Results Our developed registration method can accurately register histological and MR images. It yields results comparable to manual registration, in terms of landmark distance and volume overlap. It also outperforms both affine registration and boundary-guided registration methods. Conclusions We have developed a novel method for deformable registration of histological and MR images of the same prostate. Besides the collection of ground-truth cancer information in MR images, the method has other potential applications. An automatic, accurate registration of histological and MR images actually builds a bridge between in vivo anatomical information and ex vivo pathological information, which is valuable for various clinical studies

High-speed multicolor microscopy of repeating dynamic processes

Images of multiply labeled fluorescent samples provide unique insights into the localization of molecules, cells, and tissues. The ability to image multiple channels simultaneously at high speed without cross talk is limited to a few colors and requires dedicated multichannel or multispectral detection procedures. Simpler microscopes, in which each color is imaged sequentially, produce a much lower frame rate. Here, we describe a technique to image, at high frame rate, multiply labeled samples that have a repeating motion. We capture images in a single channel at a time over one full occurrence of the motion then repeat acquisition for other channels over subsequent occurrences. We finally build a high-speed multichannel image sequence by combining the images after applying a normalized mutual information-based time registration procedure. We show that this technique is amenable to image the beating heart of a double-labeled embryonic quail in three channels (brightfield, yellow, and mCherry fluorescent proteins) using a fluorescence wide-field microscope equipped with a single monochrome camera and without fast channel switching optics. We experimentally evaluate the accuracy of our method on image series from a two-channel confocal microscope

Construction of a Statistical Atlas of the Whole Heart from a Large 4D CT Database

International audienceWe present in this work an efficient and robust framework for the construction of a high-resolution and spatio-temporal atlas of the whole heart from a database of 138 CT 4D images, the largest sample to be used for cardiac statistical modeling to date. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. In the proposed technique, spatial and temporal normalization based on non-rigid image registration are used to synthesize a population mean image from all CT image. With the resulting transformation, a detailed 3D mesh representation of the atlas is warped to fit all images in each subject and phase. The obtained level of anatomical detail (a total of 13 cardiac structures) and the extendability of the atlas present an advantage over most existing cardiac models published previously