1) Field of the Invention
The present invention relates to a technology for extracting a region of interest from a digital model of a tissue in an organism.
2) Description of the Related Art
A digital model of a tissue in an organism is a 3-dimensional model that stores detailed information of a structure and dynamical characteristics of each tissue in the organism. Such a digital model is accumulating great expectations because its application in medical treatment has enabled simulation of diagnosis, treatment, and surgery.
Building of such a model necessitates collection of detailed structural data and dynamical characteristics of each tissue in the organism. The process of building of the model includes mainly three steps:                1) Collection of information inside the organism as image data        2) Recognition (segmentation of each tissue)        3) Addition of dynamical characteristics to each tissue        
A dynamical simulation is carried out by, for example, finite element method, to the dynamical model that is built in this manner. Results of such stimulation depend a lot on accuracy of the image data. Therefore, accuracy of a radiographic unit that is used for collection of the image data is a major factor that affects the results of the simulation.
At present, X-ray Computed Tomography (X-ray CT) and Magnetic Resonance Imaging (MRI) are mainly used to collect the image data. However, because of the characteristics of the X-ray CT and the MRI, it is not possible to collect image data of all the tissues. For example, it is not possible to collect image data of fine parts or soft tissues. Moreover, resolution of the X-ray CT or the MRI is still not sufficient and hence, at present, it is not possible to build a satisfactory model.
For building a detailed model, it is indispensable to obtain and understand information of soft tissues, which have similar constituents. However, it is not possible to acquire this information with the X-ray CT or the MRI.
In recent years, research is being carried out to use colored information contained in actual tissues in an organism to acquire detailed data of tissues in the organism. For example, in Visible Human Project (hereinafter “VHP”) carried out by National Library of Medicine, United States of America, and Colorado University, a human body was sliced at every 0.33 millimeter and surfaces of these slices were radiographed to obtain full colored continuous cross sectional image of inside of the human body. In the VHP, the human body is first preserved by special cooling process and then cut at intervals of 0.3 millimeter each from head to toe. Each slice, after cutting, is photographed by a colored digital camera having resolution of 2000×2000 pixels and the data is acquired. Thus, this process can not be repeated so often because it is a time taking, hard, and troublesome process.
The applicant, Scientific Research Center, Japan, has developed a microscope that can obtain 3-dimensional color-information of internal structure of an organism. The organism is sliced at an interval of tens of micrometers, and the slice is photographed by a CCD camera that is placed right above the slice surface, thereby enabling to obtain full colored continuous cross sectional images without shift in the axis. With this microscope, it is possible to obtain about 10,000 continuous images in just an hour.
The digital model is built using the full colored data acquired, for example, by the VHP or the microscope developed at the Scientific Research Center, Japan. However, the use of full colored data in building of the digital model has created new problems. Firstly, the full colored image is quite different in appearance from the conventional black and white image, so that the diagnosis has become difficult. Secondly, there is an enormous increase in the information. Because of these problems, it has become difficult to perform recognition (segmentation) satisfactorily using the full colored data.
Generally, in segmentation, it is assumed that in an image, a part (region of interest) corresponding to one object (tissue of interest) has almost uniform characteristics (density, texture etc.) and the characteristics change suddenly in an area of boundary with a different object (with a different tissue). Based on this assumption, the segmentation can be performed by following two methods:                1) extracting edge of the tissue of interest in the image, i.e., edge extraction; and        2) splitting the image into parts based on some constraint, i.e., region splitting.        
Snake model implementation and level setting are examples of the edge extraction. Region growing, in which regions are combined based on some constraint, is opposite of the region splitting. Hereinafter, both the region growing and the region splitting will be collectively referred to as “region growing”.
Following publications, for example, disclose technology relating to the present invention: “Three-dimensional Digital Image Processing” by Jun-ichiro Toriwaki, published by SHOUKOUDO, and “Three Dimensional Image Processing in Medical Field—Theory and Applications—” by Suto Yasuzo, published by CORONA PUBLISHING CO. LTD.
In the edge extraction, since sudden changes in the values of the pixels are emphasized to detect a boundary between the pixels, noise in the image or complications in a shape of the tissue of interest give rise to false or discontinuous edge. Moreover, as the information becomes enormous, it becomes difficult to distinguish between the characteristics features that can be and can not be used for the edge detection. These problems become prominent as the shape of the tissue becomes complicated. Thus, the edge extraction is not a good choice.
On the other hand, the region growing has almost no influence of the noise in the image. Moreover, since a region is extracted (as against an edge) in the region growing, the overall structure of the image can easily be grasped, without studding new structures. However, if the region has a very small width (e.g., like a line) and, if the characteristics change gradually (as against abruptly), the regions that should have been spit or combined are not split or combined.
A segmentation method in which both the edge extraction and the region growing are used is known. In this method, since information about the edge is used in deciding whether to combine or split the regions, discontinuity in the edge does not much affect the result, moreover, the regions can be combined or split more accurately as compared with the region growing.
However, in the region growing, it is necessary to assign parameters in advance as control conditions for distinction of regions. Furthermore, the process cannot be completed if stopping conditions are not set to extension of region that is generated. Automatic adjustment of parameters and conditions, matching with the image characteristics is difficult and hence not yet realized. Therefore, a proper segmentation result of a complicated image cannot be expected.
Besides, an interpolation of role of parameters that do not match with the image characteristics by including an interactive correction process at each stage of distinction is proposed. The interpolation is a way of carrying out proper segmentation by monitoring of process of extraction of region and specifying and correcting of over extraction occurred due to inappropriate stopping conditions of extension of region by a person having knowledge of anatomy.
However, when the amount of information is enormously, the interpolation cannot be accepted widely as it necessitates correction process to be carried out manually.
Thus, although the region growing has many advantages over the edge extraction, the effect can be demonstrated only when specified parameters and conditions are assigned corresponding to ideal data. In normal processing, no good results can be achieved without human intervention. The full color image of inside of the organism on which research work is being done nowadays, has complicated variation of density and shape and total capacity of data is large. Therefore, it is not possible to achieve good results of segmentation by applying conventional ways as they are.
USPTO Backgrounds
USPTO Backgrounds