## 1 Introduction  
Magnetic resonance imaging (MRI) is an essential non-invasive medical imaging technique to diagnose pathologies. A significant advantage of MRI is its ability to facilitate tissue characterization [1].  
The development of tools to automate all or part of the MRI image analysis process is crucial, because MRI technologies generate a vast amount of image data needed to quickly and accurately diagnose diseases and monitor patients [2].  
Most research that applies technology to imaging focuses on pathologies rather than the image acquisition process. For instance, in brain MRI--which is a common MRI examination--most studies concentrate on the analysis of lesion, tumor, tissue, whole-brain segmentation, and subcortical structure analysis [3].  
In this context, ref. [4] introduced a new framework for segmenting various brain structures, such as white matter, gray matter, and cerebrospinal fluid, from 3D MR brain images across different life stages. The method employs the higher-order Markov-Gibbs Random Field (MGRF) for improved accuracy in segmenting brain regions, particularly in infant MRIs. Focusing on automating the real-time planning of radiological brain planes in MRI, ref. [4] used deep learning techniques to detect key brain landmarks from echo planar imaging scans, enabling fully automatic planning of brain acquisitions with high accuracy in landmark detection and comparable image quality to manual planning. An automated system was also described in [5] for planning MRI scans, specifically for lumbar and cervical regions. The system automatically detects and labels the spinal anatomy in low-quality scout images, using this information to plan scan geometries. However, an important step in the imaging process is exam acquisition.  
During MRI exams, a set of localizer images is provided at the beginning. These images are used by the MRI operator to define more precise imaging regions for subsequent detailed diagnostic image acquisitions. To properly set the region of interest (ROI), the operator must locate specific anatomies and position landmarks based on them. However, this manual slice planning is tedious and susceptible to operator error. Therefore, automating MR slice planning can minimize interoperator variability, reduce total scan duration, and improve accuracy [6].  
This challenge has been addressed over the years in various ways [7], with new solutions continually emerging [6; 8; 9]. The most recent solutions use some form of deep learning algorithm. However, deep learning requires extensive image datasets and, consequently, large computational processing power.  
The real-time automatic configuration of magnetic resonance imaging (MRI) systems is crucial to optimizing imaging protocols and ensuring high-quality images. Two prominent techniques for achieving this are the Scale-Invariant Feature Transform (SIFT) descriptors and the YOLO (You Only Look Once) neural network. This comparison aims to highlight the strengths and weaknesses of each method in the context of MRI configuration.  
Therefore, this work presents two approaches based on computer vision techniques for processing images from magnetic resonance equipment during exam acquisition. The main contribution of this work is to automate and assist the operator in decision making regarding the slice planning process on patients' body structures.  
This paper is organized as follows. The next section offers a comparative analysis of SIFT and YOLO, drawing from the existing literature. The third section details the theoretical foundations and introduces the proposed methodologies. The fourth section describes the dataset used in the experiments and discusses the results. Finally, the last section presents the conclusion.