The Background Removing Algorithm Of The Cross-Sectional Images Of The Digitized Visible Human Dataset Based On The Color Space Converting

The Digital Humans is a multimedia exploration of the Visible Human Project intended for the general public. It is the creation of complete, anatomically detailed, three-dimensional representations of the normal male and female human bodies. Acquisition of transverse CT, MR and cryo-section images of representative male and female cadavers has been completed. The male was sectioned at one millimeter intervals, the female at one-third of a millimeter interval.The long-term goal of the Digital Visible Human Project is to produce a system of knowledge structures that will transparently link visual knowledge forms to symbolic knowledge formats such as the names of body parts.As a first step, in order to align the raw images visually using coronal and sagittal reconstructions as guides, the images should be removed all background material from the cryo-sections as fully as possible without removing actual tissue.In this paper, we describe the basic Image-Segmentation techniques for the processing medical images, and then discuss and analyze the latest algorithms for removing the background of the Cross-sectional images of the digitized visible human data set.There are two kinds of segment techniques for the medical images, and the digitized visible human images as well. One is based on texture of the images, and the other one is based on the color information of the images. Too many experiments show that as for the most part of the digitized visible human data, the first way which is based on the texture of the images is more flawless, and the other way based on color information is easer and more efficiency. We should consider the different methods for the specific requirement on one image, and integrate them usually.In this paper, we also present a new algorithm to remove all background material from the cryo-sections as fully as possible without removing actual tissue at the last part. Our algorithm employed color thresholding, mathematical morphology operations...