http://ee.sharif.edu/~miap/Files/A%20Survey%20of%20Medical%20Image%20Registration.pdf

A survey of medical image registration.

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Artificial intelligence in radiology

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

/pdf/deformable-medical-image-registration-a-survey-2uee59wp1z.pdf

Deformable Medical Image Registration: A Survey

Image interpolation techniques often are required in medical imaging for image generation (e.g., discrete back projection for inverse Radon transform) and processing such as compression or resampling. Since the ideal interpolation function spatially is unlimited, several interpolation kernels of finite size have been introduced. This paper compares 1) truncated and windowed sine; 2) nearest neighbor; 3) linear; 4) quadratic; 5) cubic B-spline; 6) cubic; g) Lagrange; and 7) Gaussian interpolation and approximation techniques with kernel sizes from 1/spl times/1 up to 8/spl times/8. The comparison is done by: 1) spatial and Fourier analyses; 2) computational complexity as well as runtime evaluations; and 3) qualitative and quantitative interpolation error determinations for particular interpolation tasks which were taken from common situations in medical image processing. For local and Fourier analyses, a standardized notation is introduced and fundamental properties of interpolators are derived. Successful methods should be direct current (DC)-constant and interpolators rather than DC-inconstant or approximators. Each method's parameters are tuned with respect to those properties. This results in three novel kernels, which are introduced in this paper and proven to be within the best choices for medical image interpolation: the 6/spl times/6 Blackman-Harris windowed sinc interpolator, and the C2-continuous cubic kernels with N=6 and N=8 supporting points. For quantitative error evaluations, a set of 50 direct digital X-rays was used. They have been selected arbitrarily from clinical routine. In general, large kernel sizes were found to be superior to small interpolation masks. Except for truncated sine interpolators, all kernels with N=6 or larger sizes perform significantly better than N=2 or N=3 point methods (p/spl Lt/0.005). However, the differences within the group of large-sized kernels were not significant. Summarizing the results, the cubic 6/spl times/6 interpolator with continuous second derivatives, as defined in (24), can be recommended for most common interpolation tasks. It appears to be the fastest six-point kernel to implement computationally. It provides eminent local and Fourier properties, is easy to implement, and has only small errors. The same characteristics apply to B-spline interpolation, but the 6/spl times/6 cubic avoids the intrinsic border effects produced by the B-spline technique. However, the goal of this study was not to determine an overall best method, but to present a comprehensive catalogue of methods in a uniform terminology, to define general properties and requirements of local techniques, and to enable the reader to select that method which is optimal for his specific application in medical imaging.

/pdf/survey-interpolation-methods-in-medical-image-processing-erlouqboei.pdf

Survey: interpolation methods in medical image processing

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning-based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, which is the main scope of this survey. This requires placing a focus on the different research areas as well as highlighting challenges that practitioners face. This survey, therefore, outlines the evolution of deep learning-based medical image registration in the context of both research challenges and relevant innovations in the past few years. Further, this survey highlights future research directions to show how this field may be possibly moved forward to the next level.

Deep learning in medical image registration: a survey

Bildverarbeitung für die Medizin

Bildverarbeitung für die Medizin 1998 : Algorithmen - Systeme - Anwendungen : Proceedings des Workshops am 26. und 27. März 1998 in Aachen

Spatial registration is a major problem arising whenever several images of similar contents are to be compared. Considering translations only, two-dimensional cepstral techniques have been proven to be exact and robust against noise or intensity variations. Furthermore, the cepstral filtering is numerically more efficient than most common approaches to image registration based on cross-correlation or template matching. In a previous paper, we proposed a two- dimensional cepstrum based matching technique accessing rotations and translations. The logarithmic polar mapping of the power spectra of both images to be registered is used for the decoupling of rotations and translations (similar to the Fourier-Mellin transform). Rotations are detected first matching the mapped spectra by two-dimensional cepstrum analysis. After rotating back one image, the relative shift is determined using the same cepstrum technique. In clinical practice, the rotation detection step was discovered as the weakness of this registration technique. Based on 855 pairs of dental radiographs acquired in known positions, three different approaches of matching the mapped spectra are compared: the cepstrum technique, the cross-correlation, and the entropy of the one-dimensional histogram distribution function of the substraction image of the mapped spectra. The combination of the log-polar mapped power spectra of both x rays with the entropy-measure allows the best detection of rotations. The union with common cepstrum methods correcting translations results in a robust rotation- extended cepstrum technique.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Rotation-extended cepstrum technique optimized by systematic analysis of various sets of x-ray images

In dental implantology more than one hundred enossal implant systems are in use Once embedded, the dental x-ray examination is the most important tool for determining implants' producer, name, and type In this paper, we present a system for automatic detection and identification of dental fixtures in intraoral x rays (IDEFIX) combining common direct digital image acquisition techniques with specially designed image analysis IDEFIX can process any digital radiograph (eg RVG, Sens-A-Ray, Schick, Sidexis, Digora) as well as digitized dental films A reference database has been generated by precise measurement on the implant systems used so far (eight implants) including parameters like length, diameter, and cross section area After binarization of the current digital x-ray image, a parameter set is extracted from each detected object applying mathematical morphology All objects are classified using a simplified nearest neighbor method and the Euclidean distance metric If the distance of the objects' parameter set to one of the reference sets is below a given threshold, name and type of the identified dental fixture are displayed on the screen Otherwise, the actual object will be rejected as a no-implant IDEFIX has been evaluated by processing various in-vitro acquired radiographs Different implants were classified captured with identical conditions as well as acquired varying the angulation of the x-ray tube It is shown that misangulations up to twenty degrees are tolerable preserving correct identification Other image structures like teeth or fillings result in large distances to all reference parameter sets and, therefore, they are reliably recognized as non-implants

IDEFIX: identification of dental fixtures in intraoral x rays

An adaptive Wiener-filter image restoration approach is being described. It is exemplarily applied to intraoral radiographs generated by two x-ray-sensitive CCDs, one having an additional scintillator screen. The devices' MTFs were determined by means of a slightly angulated slit according to DIN 6867/2. Noise power spectra at various exposures were measured and decomposed into quantum and detector noise with respect to the signal and noise model. Both noise power levels were proportional to the incident exposure. Wiener filters for high, low and medium exposures were designed. Weight tables for superpositions of these filters were calculated by minimizing the mean square errors between the superposition MTFs and Wiener filter MTFs for arbitrary exposures. The pixelwise weights depending on the local mean were calculated after a preceding lowpass filtering. The final restored images were composed from spatially invariant filtered images generated by 2D-IIR filtering. The results indicate that the presented adaptive restoration method is a promising approach with regard to both image quality and processing speed demands.

Thomas Martin Lehmann

Papers

Bildverarbeitung für die Medizin

Bildverarbeitung für die Medizin 1998 : Algorithmen - Systeme - Anwendungen : Proceedings des Workshops am 26. und 27. März 1998 in Aachen

Rotation-extended cepstrum technique optimized by systematic analysis of various sets of x-ray images

IDEFIX: identification of dental fixtures in intraoral x rays

System analysis of x-ray-sensitive CCDs and adaptive restoration of intraoral radiographs