In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

This chapter introduces the combination of wireless body area network and mobile cloud computing in healthcare. The increased growth of low-power integrated circuits, physiological sensors and wireless communication has introduced a new generation of wireless sensor networks. Cloud computing is on high demand, whereas in case of mobile cloud computing the device is much more user friendly to manage the information. The combination of wireless body area network (WBAN) and mobile cloud computing (MCC) promises a better performance to the users immediately. It is more feasible to wire a sensor which performs the required medical tests and provides the information through devices like mobile phones and tablets. In this chapter, a theoretical study on the combination of WBAN and mobile cloud computing has been done.

The ad hoc routing protocol's design has received a huge attention due to the unpredictable and rapid mobility of a node. It is created dynamically without any infrastructure. In ad hoc each node is responsible for routing the information between them. To improve the performance of unused information and to overcome the overhead in maintaining this information the protocols were designed. MANET (Mobile Ad hoc Network) is the collection of wireless mobile nodes which can dynamically form a network. By this definition we can conclude that there is no centralized administration, permanent topology and standard support services. Rough set theory is a computing technique to deal with uncertainty and vagueness. The notion of the thresholds and the temporal extensions to Rough Sets was applied in several protocols. The successful routing in MANETs using the Random Waypoint mobility model was based on various rough sets based protocol.