Case Study Of MRI

background: Multiple sclerosis (MS) is the most common inflammatory demyelinating disease of the CNS and the most frequent cause of nontraumatic neurological disability in young and middle-aged adults (1). Women are twice as likely to be affected as men, and onset typically occurs between the ages of 20 and 40 years (2). The diagnosis Depend on a detailed history; careful neurological examination; and supportive paraclinical investigations. According to the new McDonald criteria, the diagnosis of MS requires objective evidence of lesions disseminated in space and time.MRI findings may contribute to the determination of dissemination in time or space (4).

The diagnosis of MS has traditionally relied upon accumulation of information, clinical and paraclinical, that leads to a positive diagnosis and can help to eliminate alternative

Multiple sclerosis (MS) is the most common inflammatory demyelinating disease of the CNS and the most frequent cause of nontraumatic neurological disability in young and middle-aged adults (1). Women are twice as likely to be affected as men, and onset typically occurs between the ages of 20 and 40 years (2). The diagnosis Depend on a detailed history; careful neurological examination; and supportive paraclinical investigations. According to the new McDonald criteria, the diagnosis of MS requires objective evidence of lesions disseminated in space and time.MRI findings may contribute to the determination of dissemination in time or space (4).)

Apart from demyelination, neuronal pathology is largely responsible for brain atrophy. Now a days , cortical lesions and brain atrophy have emerged as new pathological markers of MS Progression. GM damage was first reported by Sander in 19th century . About 26% lesions are found in cortical and subcortical GM. In a study by Dalton et al, no WM loss and increasing GM loss was seen in patients with developing MS over 3 years following a CIS. The immunohistochemical staining techniques differentiates 4 major categories of cortical lesions: 1) lesion involves deeper layer of GM and adjacent WM (mixed lesions), 2) intra cortical lesions, and 3) extend from pial surface into cortex, 4) subpial lesions.

Criteria that determine “MS” have been revised several times in the last few years (2001, 2005, and 2010) (Polman, 2011). Criteria now enable diagnosis of MS when new MRI lesions define separation in time and space i.e., a new MRI lesion more than 30

Segmentation evaluation is the task of comparing two segmentations by measuring the distance or similarity between them where one is the segmentation to be evaluated and the other is the corresponding groundtruth segmentation. The performance of the proposed SM is validated with breast overlapping metrics such as Dice Index (DI), the Jaccard index (JI), false positive (FP) and false negative (FN) errors. The DI [43] [44], also called the overlap index, is the most used metric in validating medical segmentation results. It provides a direct comparison between automatic and ground truth segmentations. It can be calculated by using the following equation:

In the world of today complex medical emergencies are becoming more and more common. Physics plays a major role in the technology that we use to accurately diagnose these medical cases in a time effective manner. Many different physics concepts are involved in the MRI and CT scanning process and without proper understanding of these concepts and how they works it would not be possible to have the medical equipment that is available today. These concepts include beam hardening and dual-energy, filtered back projection and helical, dose measurement, tissue contrast, spatial localisation, pulse sequences, chemical shift phenomena and diffusion-weighted imaging.

Moreover, segmentation should be defined through different methods, each of those enhancing distinctive features of the target group:

The main research problem in the project is how to utilize ontology for image semantic extraction and interpretation in order to reduce the semantic gap in image analysis and retrieval. In order to better understand the problem some questions should be in mind. How an image is segmented into regions which correspond to the real object? What concepts, textual and/or perceptual concepts, should ontologies contain? How abstract semantics are inferred from image contents based on contextual knowledge in ontologies?

Computerized pictures are a standout amongst the most key medium of passing on data. Extricating the data from pictures and comprehension them such that the removed data can be utilized for a few assignments is a vital normal for Machine learning. Utilizing pictures for the route of robots is a case of the same. Different applications, for example, extricating defame tissues from the body checks and so forth frame an indispensable piece of Medical conclusion. Picture division is one of the introductory strides in course of comprehension pictures and afterward finds the distinctive items in them.

Segmentation is used mainly to target a certain group from within a population. Psychographic segmentation is one which uses peoples lifestyle, their activities, interests as well as opinions to define a market segment. Psychographic segmentation is quite similar to behavioral segmentation. But psychographic segmentation also takes the psychological aspects of consumer buying behavior into accounts. These psychological aspects may be consumers lifestyle, his social standing as well as his AIO.

Using the five bases of segmentation, the ones that have been utilized by the advertisements include;

In region-based segmentation method, a seed point is selected as our starting point. The pixel intensity of the seed point is used for the initial condition, pixels with similar features are found to be in the same region [11]. As the size of the region increases with the addition of nearby pixels, a region growth take place and this process is referred as region growth process. Thus region-based segmentation methods are more suitable for situations where the background environment and the shape of the foreground may change continuously.

To extract the small and large images by using the method of detecting edges of some predefined range of orientation in order to remove the problem related to detecting such boundaries.

Various classification methods have been developed to extract information from imageries. The two main types are pixel and object-based methods. Pixel-based methods can be cluster based unsupervised or supervised classification whereas the later uses statistical (e.g., maximum likelihood algorism) and non-statistical algorithms (e.g., support vector machines) (Lu, Li, Kuang, & Moran, 2014). The object-based classification which overcomes some of the particular problems encountered with pixel-based classification (Blaschke, 2010) was used to analyze