## 1 Introduction  
Image processing, that is image manipulation and information extracting from sets of images, is widely used today in the field of medicine [1]. One fast-growing type of application is the use of image processing to improve medical diagnostic effectiveness [2]. Diagnostics that rely on imaging technologies such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and X-ray benefit considerably from the practice of image processing. Images taken using these clinical procedures can be improved by post-processing algorithms that enhance image quality using contrast enhancement and noise reduction [1], or automatically extract necessary information, as done in image segmentation [1; 2]. While most of these tools are implemented as automated detection tools [2], the final decision is always done by an experienced diagnostic physician. The amount of images generated by imaging technologies today is growing exponentially. This fact can heavily burden the decision-making process done by the diagnostic physician. Therefore, a separate group of tools may be required, namely tools that reduce the amount of unnecessary images, and support a short and simple diagnostic process. In this study, we demonstrate how such applications, which consist of image-processing algorithms, can facilitate a pronounced improvement of the diagnostic process. Specifically, we chose the case of magnetic resonance (MR)-based diagnosis of multiple sclerosis (MS). MS is an inflammatory demyelinating autoimmune disease that affects the central nervous system and afflicts over 2.5 million people worldwide. It is considered one of the leading causesof neurological-based disability in young adults [3; 4] and the most common disease of the central nervous system that is characterized by the presence of white matter (WM) lesions [5; 6]. The clinical diagnosis of MS is based on a patient's description and on findings in neurological tests. A central non-invasive diagnostic test of MS is done using MRI [7]. Conventional MRI techniques are highly sensitive in detecting brain abnormalities (e.g., MS lesions) and can provide quantitative assessment of inflammatory activity and lesion load [7]. MRI-derived metrics have become established as the most important para-clinical tool for a variety of applications, such as brain lesions diagnosing and monitoring [8], longitudinal studies of cognitive aging [9], or even the analysis of fetal brain development [10]. According to the [PERSON] criteria, diagnosis of the disease requires finding MS lesions in at least two separate areas of the brain [6; 11; 12], and several images are sufficient for diagnosis. MRI images offer a high contrast between the main types of brain tissues, namely gray matter, white matter, and cerebrospinal fluid. Many different types of MRI tests exist [6; 13; 14]. Also, different MR pulse sequences produce images with different appearances, highlighting different tissues because of sensitivity to naturally occurring (endogenous) contrast or exogenous contrast (i.e., intravenous gadolinium contrast agents), and elaborate on FLAIR etc. Turbo inversion recovery magnitude (TIRM) is a type of inversion recovery MRI pulse sequence. However, not all of them are relevant for MS diagnosis. While previous studies mostly used the FLAIR images together with either T1 or T2 weighted images [15; 16], in this paper we use a wider range of MRI image types [17; 18; 19; 20], including T2 FLAIR, T2 FSE, T2 TS, T2 TIRM and T1 FL2D. The use of computer-assisted detection/diagnosis (CAD) in other fields such as mammography [21; 22] is well known. Current MR consists of 500-700 images of various types for each tested subject; as many as thirty for a single type (accounting for the different coordinates of the virtual brain slices). Additionally, within images that belong to a suitable type, a significant portion is concerned with brain areas that are irrelevant for the diagnosis (i.e., images of areas without WM lesions).