Essay examples --

In the given constraints of Cost, Bulkiness and Portability we will design a smart phone based Fundus imaging device. Device application software will solve the more problem like: Automatic Glaucoma Detection and Image Enhancement problem so that its doesn't required trained user for operation. That will be possible by embedding the Optical(image capturing unit), Electronic (Microprocessor), Communication media(Wi-fi,Blue-tooth,Web etc) and Data base in a single device (Figure 2.1). This section will capture magnified fundus image in CMOS Camera. To make its possible by interfacing direct ophthalmoscope with CMOS Camera.Ophthalmoscope is an instrument that is use for examining the interior structures of the eye, especially the retina, consisting essentially of a mirror that reflects light into the eye and a central hole through which the eye is examined. Here it will interface device and human eye. We will used smart phone high resolution ($>$ 5MP) Camera for the same. Use a mobile phone holder (Rapid Prototype Model) to mount Mobile phone camera on ophthalmoscope. section{Image Accusation and Data management} Optical image of the fundus is converted into digital form by Mobile phone inbuilt Camera. Initially capture image/video is saved in local on board memory or SD Card. This section handles the data organization and management work such that we can easily find out any patient data.To organized data it will do flowing task: egin{enumerate} item{Create Folder whose Name is same Patient ID} item{Save Image/Video in that folder} item{Right and Eye Information is used to do file naming .} end{enumerate} section{Image Processing} This section will perform all the image processing task, that is widely separated into two ... ...paration of around 10 mm. The ophthalmoscope used provides an easy entry into the eye, together with a wider field of view to better observe eye conditions. paragraph{} The unit captures the fundus image in a JPEG format, which gets stored in the phone. The image is then processed for detection of its optical disk and cup, after which the respective areas are calculated to compute the CDR for setting up the threshold for the affected eye. The image processing operations as discussed above are inbuilt in the Android Application. paragraph{} The setup for capturing the fundus image of a person's eye using the developed system is shown in figure 2.2. The system is held very close to the person's eye to be detected for Glaucoma. The real time application on the phone displays the image. Also, it prints out the result in a format which can easily be interpreted.