In this work a genetic algorithm based approach for segmenting the parts of brain MRI (Magnetic Resonance Imaging) image slices has been presented. Segmentation of the brain MRI image has been a challenging task and an open area for research off late due to reason that, the intensity differences between the different regions present in the image is very less. Hence a complete automation of segmentation process is difficult. In this work the various parameters of the genetic algorithm has been analyzed and an optimized threshold value has been determined based on the slice type. The complexities in the segmentation algorithm and the challenges have also been reported.

This paper presents a survey of advanced methods in segmenting the MRI (Magnetic Resonance Imaging) image of the brain. Segmentation of the brain is a challenging task because it requires more emphasized methods to differentiate each of the regions present in the brain image. The intensity differences between the different regions in the brain MRI image are very less, making it difficult to automate the entire segmentation process. Hence, a thorough understanding of the existing segmentation algorithm is essential for accurate segmentation. The segmentation algorithms surveyed in this work are Neural Network Model, Self-Organizing Maps, Radial Basis Function, Back Propagation, Fuzzy C-Means, Deformable Models, Level Set Models, Genetic Algorithm, Differential Evolutionary Algorithm, Hybrid Clustering and Artificial Intelligence. Such a survey would be helpful for researchers working in the field of brain image segmentation. The paper discusses the complexities in the segmentation algorithm and also the challenges in segmenting the brain MRI images. The segmentation outputs and analysis of the existing literature has also been discussed. The major criteria and their advantages in segmentation of each algorithm have been reported accordingly in the observations.

This paper presents to detect the faces in an image and locates the facial features in an image. The detection of the facial parts such as eyes, nose, mouth and face is an important task in this process. This system is used to recognize and detect the parts of the human facial factors in an image. The study involves the algorithm of Viola-Jones Cascade Object Detector which gives various combination of filters and methods to detect these facial expressions.