Medical image Classification Technique
Medical image Classification can play an important role in diagnostic and teaching purposes in medicine. For these purposes different imaging modalities are used. There are many classifications created for medical images using both grey-scale and color medical images. One way is to find the texture of the images and have the analysis. Texture classification is an image processing technique by which different regions of an image are identified based on texture properties. Second way is by using neural network classification techniques and the final one is by using the data mining classification schemes. Neural networks play a vital role in classification, with the help of, supervised and unsupervised techniques. The word data mining refers to, extracting the knowledge from large amounts of data. It is one of the area, which uses statistical, machine learning, visualization and other data manipulation with knowledge extraction techniques. This finds an insight into the relationship between the data and patterns hidden in the data. Using the digital data within the pictures actual communication systems creates a possibility for research enhancements. Medical images form a vital component of a patient’s health record and are associated with manipulation, processing and handling of data by computers. This makes the basis for the computer-assisted radiology development. Further developments are associated with the use of decision support systems which helps to decide, the relevant knowledge for diagnosis
Effective medical images can play an important role in aiding in diagnosis and treatment; they can also be useful in the education domain for healthcare students by explaining with these images will help them in their studies. Advances in digital imaging technologies, created a large growth in the number of digital images taken, in recent years. In addition to the Picture Archival and Communication Systems...