1 "3Q Factors to Enhance Big Data Medical Image for Better Diagnosis" Raflaa Hilmi Hamid, Ahmed Nabeel Ahmed, Hasanain Mohammed Manji Under the supervision of Dr.Azizah Bt Haji Ahmad College of Arts and Sciences University Utara Malaysia Abstract — Nowadays, images are employed in several areas of medicine for early diagnosis. In this sense, the industry provides accurate models to obtain X-ray, Computerized Tomography (CT), Magnetic Resonance Imaging (MRI) and others of high resolution equipments. However, other images, such as those related to pathological anatomy present in many situations poor quality, big quantity of information to be process and non- quick in enhancement. This complicates the diagnostic process. This work is focused on the quality, quantity and quickness enhancement of this type of images through a system based on informatics image system combined with traditional techniques of image processing. The results show that the proposed methodology can help medical specialists in the diagnostic of several pathologies. Considering that the medical image as a big data issue. Keywords— super-informatics, medical imaging, diagnosis, image processing, big Data. I. Introduction To analyze the application values of the super-informatics image technique system (SIITS) based on picture archive and communication system (PACS) in improvement of medical imaging (MI) diagnosis and also image processing techniques [23]. In normal case MI should compare with multimillion images in one of the informatics system Vendor Natural Archive (VNA) until this system find the conformable diagnosis that fit this image, this process will take a lot of time and effort and also high cost which made this techniques seem to be slow, unproductive and ineffective [17]. For many years, big data informatics system developed, but also the MI gets more big and big, spatially with the urgent need to get early diagnosis for disease that contact with human life. The health care organizations spend more and more every year in order to be advanced by one step in this field, since early diagnosis for the disease such as cancer and blood disease is the key of recovery and survival [6]. An archive is a location containing a collection of records, documents or other materials of historical importance. An integral part of picture archive and communication system (PACS) is archiving. When a hospital needs to migrate a PACS vendor, the completed earlier data need to be migrated in the format of the newly procured PACS. It is both time and money consuming [23]. PACS was consisted of medical imaging and data acquisition components and storage and display subsystems. Different imaging modalities in modern imaging system (e.g., X-ray, Ultrasonography (US), Digital Subtraction Angiography (DSA), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI), positive emission tomography (PET)) could be processed in PACS in the format of Digital Imaging and Communications in Medicine (DICOM) imaging. The PACS was an integrated system, allowing for efficient electronic distribution and storage of medical images and access to medical record data. PACS of different size were widely used in clinical research stage and diagnostic imaging. With the rapid development of imaging technology X-ray, US, DSA, CT, MRI, PET and other modern imaging devices formed a huge medical imaging system (Big Data). One of the most important characteristics of modern imaging system was mass information and native digital images. As a result, the pattern of medical imaging education should change a lot of correspondingly. The foundation of medical imaging system was high-quality, a large quantity and quick process of imaging data. It was also well known that the diagnosing of medical imaging requires systematic study of a large number of medical images [1]. At presents, the potentials of PACS for diagnosing applications were not fully understand by