Computer Aided Analysis of Filters Using Level Set segmentation for Biomedical Images

Abstract

Medical imaging allows scientist and physicians to decide about life saving information regard to the human physiological activities. It plays an important role in the diagnosis, therapy and treatment of various organs, tumors and other abnormalities. Image segmentation is typically used to locate objects and boundaries in images and should stop when the object of interest in an application have been isolated .It is used to calculate the geometric shape and size of tumors and abnormal growth of any tissue. There are many techniques available for auto-segmentation of images like Active contours, Fuzzy based classifiers, Gradient Vector Field theory, Tensor based segmentation, Level set theory etc. But many of them are suffering from problems like optimization, initialization and insufficient results in noisy images. Most widely used segmentation is level set segmentation in biomedical medical images such as X-ray, CT and MRI. In this work we have applied various image filtering techniques to modify or to smooth the image and to enhance the efficiency of previous algorithm. Therefore, the use of filter depends upon the type of images used for the calculation of tumor size and shape, in the X- ray, CT scan and MRI images. For the evaluation of the performance of filters the following parameters like signal to noise ratio, peak signal to noise ratio, weighted peak signal to noise ratio, entropy and energy measure are used and the MATLAB codes required in calculating these parameters are developed. These parameters are used to calculate the image quality of the output image obtained from above mentioned filters. However, only a few filters gave good results. We tried to calculate the parameters of the segmented part and optimum filter for above said purpose.