Medical Image Analysis

Image analysis has an important role in many applications ranging from medical imaging to astronomy. It is the process of identifying and understanding patterns that are relevant to the performance of an image based task. It is different from other image processing applications like restoration, enhancement and coding where the output is another image. The image is processed in such a way that it removes all unwanted information retaining only that part of the image which contributes significantly to the analysis task. The analysis task creates a mapping from a digital image to the description of image's content. The image description can either be a number which would represent the number of objects in the image, or it can be a degree of anomaly which would define the shape variation of an object or it can be labelling of pixels to classify different regions of the image. Until early 90s, image processing was performed with specially designed and constructed areas of memory called framestores and the performance and speed were rather slow. But, now it is possible to download image analysis software from a number of websites and perform the task of analysis with less effort. There are three levels in image analysis. In the low level processing, functions that may be viewed as automatic reactions are dealt where as intermediate level processing deals with the task of extracting regions in an image that results from a low level process. High level processing deals with recognition and interpretation tasks. Image analysis is performed using either bottom up approach or top down strategy. In bottom up approach, low level features are extracted from the raw image data and later, this is processed in higher levels. In top down approach, the image characteristics are hypothesized at the highest level and is proceeded towards the lower level until the raw image has been reached (Gonzalez and Woods, 2000; Mantas, 1987). Image analysis involves the study of feature extraction, segmentation and classification (Jain, 1995).