A Review on: “Image Segmentation Based on Level Set Method”
The challenges in image segmentation for real world images, is ‘intensity Inhomogeneity’. As the presence of intensity inhomogeneity or non-uniformity is seen in the image, the image segmentation results are not up to mark. The existing image segmentation algorithm depends intensity value i.e. discontinuity & similarity. The dissimilarities of image intensity is often seen in the region of interest (ROI), which makes researcher to use level set method for image segmentation. Intensity Inhomogeneity depends on spatial variation on illumination, interference of imaging devices and sometimes on noise and of low contrast. Intensity non uniformity also seen, as the overlaps between the range of intensities in the region which to be segmented is present in image. So it is very difficult to segment the image in presence of intensity Inhomogeneity. In human vision, the complex image is immediately segmented into the simple objects on the basis of color, texture, patterns, shapes and etc, which is not an easy task if processing is done on digital computer platform as computer not familiar with image pattern, texture, and 3D geometry. etc. And so image segmentation is a big challenge in the area of Image processing, like Satellite Image Processing, Object detection, Recognition Tasks, Surveillance based on image / video, Image enhancement, Biomedical Image Processing etc. Now a days the use of level set method in image segmentation techniques has been tremendously increase. This literature review provide a brief overview of most common segmentation techniques, and a comparison. Our aim is to implement a level set approach for image segmentation.
Level Set Method; Image Segmentation; Intensity Inhomogeneity; Contour.
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