This paper discusses the challenges of maintaining consistent image sequences during patient examinations. One of the key issues that arises during MRI is the patient’s inability to maintain a constant position, thus resulting in image shifts that impede image fusion and subsequent diagnostic quality. To address this, coregistration is employed, which involves aligning two or more images based on a reference image to minimize shifts between them. This is a crucial step in preparing data for analysis and can lead to previously overlooked images becoming important components of medical solutions. In this study, a novel measurement of coregistration effectiveness is presented that includes normalization, difference, Top-hat transformation filtering, and global thresholding to achieve image similarity. Mean squared error and the structural similarity index measure are utilized as references against which the described method is compared. The outcomes of the measurements are presented and discussed. The results demonstrate the efficacy of the proposed metrics of coregistration effectiveness in facilitating a more accurate and comprehensive analysis of MRI data. Due to the achieved results, the main purpose is feasible.
The anticipated expansion of TinyML-related technologies will require methods for efficient ML model development. In this paper, we present a method for obtaining a set of ML models for TinyML systems that satisfies the assumption that a more efficient model is also more complex and therefore consumes more energy. The results show that our method is capable of providing numerous diversified sets of ML models.
A novel approach to syntactic pattern recognition based on multi-string parsing is introduced in the paper. A methodological motivation of the research, which has resulted from application-oriented requirements, is presented. Formal foundations, an architectural model and a generic recognition algorithm of the approach are introduced. The approach allows one to recognize complex objects which should be represented by a series of images instead of one image. The series of images is represented with a sequence of aspectual patterns which are treated as a holistic aspect-integrated description of a recognized object.
Computed tomography (CT) images are widely used in medical examination applications. They are constructed from the raw data using different kernels. However, due to the technology used, the software finds problems with image segmentation in the presence of noise or blurred edges. In this paper, we use quaternion mathematics for image de-noising by applying the bilateral filtering algorithm to reconstruct a pure soft image from the images reconstructed with sharp kernels, without using the raw data. Our output is compared to reconstructed images with different denoising techniques, such as non-local means and wavelet denoising. The reconstructed images using other techniques are assessed using structural index similarity (SSIM) and peak signal-to-noise ratio (PSNR). Results show high similarity by comparing the outputs of sharp kernels to that of the B30f. Furthermore, the results show that the proposed work can efficiently increase the similarity between the images reconstructed with hard and soft kernels.
Relative representations allow the alignment of latent spaces which embed data in extrinsically different manners but with similar relative distances between data points. This ability to compare different latent spaces for the same input lends itself to knowledge distillation techniques. We explore the applicability of relative representations to knowledge distillation by training a student model such that the relative representations of its outputs match the relative representations of the outputs of a teacher model. We test our Relative Representation Knowledge Distillation (RRKD) scheme on supervised and self-supervised image representation learning with MNIST and show that an encoder can be compressed to 47.71 https://github.com/Ramos-Ramos/rrkd .
