Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process.

Evolutionary algorithms have been used recently as an alternative in image registration, especially in cases where the similarity function is non‐convex with many local optima. However, their drawback is that they tend to be computationally expensive. Trying to avoid local minima can increase the computational cost. The purpose of authors’ research is to minimise the duration of the image registration process. This paper presents a method to minimise the computational cost by introducing a machine learning–based variant of Harmony Search. To this end, a series of machine‐learning regression methods are tested in order to find the most appropriate that minimises the cost without degrading the quality of the results. The best regression method is then incorporated in the optimisation process and is compared with two well‐known ITK image registration methods. The comparison of authors’ image registration method with ITK concerns both the quality of the results and the duration of the registration experiments. The comparison is done on a set of random image pairs of various sources (e.g. medical or satellite images), and the encouraging results strongly indicate that authors’ method can be used in a variety of image registration applications producing quality results in significantly less time.

Intravascular ultrasound (IVUS) imaging offers accurate cross-sectional vessel information. To this end, registering temporal IVUS pullbacks acquired at two time points can assist the clinicians to accurately assess pathophysiological changes in the vessels, disease progression and the effect of the treatment intervention. In this paper, we present a novel two-stage registration framework for aligning pairs of longitudinal and axial IVUS pullbacks. Initially, we use a Dynamic Time Warping (DTW)-based algorithm to align the pullbacks in a temporal fashion. Subsequently, an intensity-based registration method, that utilizes a variant of the Harmony Search optimizer to register each matched pair of the pullbacks by maximizing their Mutual Information, is applied. The presented method is fully automated and only required two single global image-based measurements, unlike other methods that require extraction of morphology-based features. The data used includes 42 synthetically generated pullback pairs, achieving an alignment error of $0.1853$ frames per pullback, a rotation error $0.93°$ and a translation error of $0.0161$ mm. In addition, it was also tested on 11 baseline and follow-up, and 10 baseline and post-stent deployment real IVUS pullback pairs from two clinical centres, achieving an alignment error of $4.3\pm 3.9$ for the longitudinal registration, and a distance and a rotational error of $0.56\pm 0.323$ mm and $12.4°\pm 10.5°$, respectively, for the axial registration. Although the performance of the proposed method does not match that of the state-of-the-art, our method relies on computationally lighter steps for its computations, which is crucial in real-time applications. On the other hand, the proposed method performs even or better that the state-of-the-art when considering the axial registration. The results indicate that the proposed method can support clinical decision making and diagnosis based on sequential imaging examinations.

This survey covers a great deal of the whole problem regarding the methods of medical image registration. First of all, the work presented is this paper is discriptive and meticulous. The number of articles referenced by the author support the articl...

Artificial intelligence (AI) is becoming increasingly used in radiology over the past years due to the astonishing variety of tools and plugins intended for image analysis and modeling. Specifically, the use of radiomics, describing the development of imaging signatures from a number of handcrafted imaging features, is gaining ground since it has shown great promise toward accelerating personalized diagnosis and therapy in healthcare. A de novo synthesis of a radiomics analysis pipeline can be technically complex and requires a carefully designed step-by-step analysis infrastructure, most frequently conducted by AI experts. However, the end result and the benefit of such an analysis can be accessible to an extended number of professionals through the use of user-friendly software tools that do not demand explicit knowledge of the theoretical framework. In view of the above, the aim of this chapter is not only to shed light on the technical and theoretical aspects in radiomics but also primarily to provide the clinical experts with a comprehensive list of user-friendly and readily available tools and plugins to design and experiment on their autonomous end-to-end radiomics analysis workflows. Keywords Artificial intelligence Machine learning Image modeling Image preprocessing Image segmentation Deep learning Radiomics