Fusion of multiple features in Magnetic resonant image segmentation using genetic algorithm

Abstract

In healthcare applications, there is tremendous growth in using the computer assistance for effective and fast diagnostic. There are various modalities such as Magnetic resonance imaging (MRI), computed tomography (CT), digital mammography, and others, to provide an insight of subject's body, noninvasively in order to facilitate diagnostic stakeholders to take decision in diagnosis. Being an important step of imaging systems in diagnostic, MRI imaging has been active area for researchers in computational intelligence and image processing. One of the most important problems in image processing and analysis is segmentation and same is true for biomedical imaging. The main objective of segmentation is separating the pixels associated with different types of tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). In this paper, we attempted to optimize the feature set constructed from more than three different types of features. It is well-known fact that, long feature vector representation can be boosting the performance. However, irrelevant feature elements from the long feature vector can become hurdle in convergence of classifier. The optimization feature vector is accomplished using genetic algorithm (GA) with an objective function of maximizing the sum of precision and recall. In addition to the elimination of the feature elements, some elements were also weighted to reduce their effect in the feature matching score. This overall process can also be considered as “fusion of features” for MRI segmentation.