Image Processing is one of the most challenging applications of Artificial Intelligent. It refers to various operations that can be applied to the image. It implied image to mage transformation. In computer vision, "image" usually refers to recorded image, such as a digital image, picture or video image. Images are needed in many real-life applications. Images play important role in information exchange, especially when a situation where requires very long language based description; it can be detailed figure with a set of pictures or a film in a very short time. Primitive issues in a real life image processing include image acquisition, image restoration, image compression, image enhancement image analysis and so on.
Image processing plays important roles in many real life applications. In the same time, many problems arise due to the image's "unstructured" nature. (Sengupta .K, n.d.) The simplest of vision problems can be exacerbated by artefacts like the high level of noise introduced by CCD cameras, the effects of shadows cast by objects, changes in the lighting condition and so on.
It is a difficult task to solve vision problems despite such problems, especially in a robust and generic fashion. Thus, numerous technical questions remain unanswered, which is reflected in the limited success of vision systems in industry today. It is one of the reasons why many research areas about image processing take place nowadays; one of them is in medical field.
Based on current research, there are many new technologies being introduced. For example, doctors nowadays can easily detect and diagnosis cancer or other diseases from patients with the aid of optical imaging. The most commonly and commercially available Archiving and Communication System (PACS) installed in many hospitals collects a large amount of digital biomedical image data monthly, weekly even daily. However, the utilization of such...