The present invention relates to a method of and an apparatus for processing multi-dimensional data, and in particular, to a method of and an apparatus for achieving a region extraction efficient for conducting an individual internal organ extraction, which is essential when presenting a three-dimensional display of individual internal organs by use of three-dimensional digital data attained through a shooting operation by an MRI, an X-ray CT, and the like.
Known examples related to the present invention have been described in the following references (1) to (3).
(1) Azriel Rosenfeld, "Digital Picture Processing"
(2) U.S. patent Ser. No. 07/602817
(3) U.S. patent Ser. No. 07/797893
Methods of extracting a particular region from given data have commonly been classified into two large groups.
1 Method of classifying regions according to a property (for example, density) of each pixel thereof
2 Method of extracting regions by use of spatial continuity thereof.
For example, in a case where a bone region is extracted from an X-ray computer tomography (CT) image, since the pixel values (CT values) of the bone region are higher than those of the other regions, the method 1 above can be utilized. On the other hand, in the image attained by a magnetic resonance imaging (MRI) system, the individual regions cannot be easily identified depending on pixel values, and hence the extraction is carried out according to the method 2 above.
As specific methods associated with the method 2, there have been well known an extraction method called "edge trace" achieved by tracing a region boundary and an extraction method called "region growing" accomplished by expansion of a concatenated region. Results of the extraction conducted according to the edge trace method are attained as boundary lines or surfaces; consequently, this method is suitable for a high-speed three-dimensional display using a surface model. On the other hand, the boundary is likely to be interrupted due to noises or the like. In consequence, a correction operation is inevitably required to be carried out by the operator on the actual image data.
In contrast therewith, according to the region growing method, the concatenated region is expanded from an internal portion of a region to a periphery thereof. In the above reference (1), a basic method of region growing has been described. In this method, the region expansion, which is hindered by noises in the edge trace method, can be achieved without such a hindrance and can be easily applied to three-dimensional data. In consequence, the extraction method using the region growing technology is considered to be appropriate for the MRI three-dimensional image.
In the region growing method, the contour and shape of the extraction region vary depending on an extraction (expansion) condition established to judge a concatenation feature. Consequently, in order to increase reliability of extraction, it is necessary to optimize the expansion condition for each image and each extraction objective region. In the reference (2) above, there has been disclosed a method in which reliability of extraction processing is improved by employing an expansion condition including a combination of a local condition and a global condition.
In the region growing method, after a growth start point and a judge reference are supplied at an initiation point thereof, there is not inherently required any human power. This is most ideal as for automation of extraction. However, according to this method, when the expansion point once enters another region, the extraction is commenced in the entered region and hence the operation achieved only by the operation leads to a problem of reliability. Actually, even when the region growing method is directly applied to image data containing noises and having non-uniform density, a good result is rarely expected.
In the reference (3) above, there has been described a support method which can be considered to be effective for improvement of extraction reliability and which is achieved by an operator for the region growing method. In this method, an extraction process of the region growing operation for the three-dimensional voxel data is continuously monitored on a three-dimensional display image such that when the expansion point enters a region other than an objective region, the region expansion is immediately stopped so as to delete the region by a function to correct three-dimensional voxel data.
The most difficult problem of the concatenated region expansion method including the region growing method as its representative method is that when the extraction objective region (to be referred to as "an objective region" herebelow) is concatenated even via a point with a region (to be referred to as "an external region" herebelow) external with respect to the objective region, the expansion point may enter the external region and the region extraction is commenced. As methods of preventing the expansion point from entering the external region, there have been considered several ideas as follows.
(1) Setting more a severe judgement reference (expansion condition) of concatenation feature
When the expansion condition of the concatenation feature is set to be more severe, the probability of the expansion point entering the external region from the extraction objective region is accordingly increased. However, when the judgement reference is set to be more severe, it becomes more difficult to extract the entire objective region. To overcome this difficulty, there have been considered the following methods (reference is to be made to reference 2).
1: A plurality of judgement references are established to conduct judgement of the expansion point in a multilateral manner.
2: The region of extraction data is uniformly expanded toward its periphery.
(2) Correcting data being subjected to concatenated region expansion
In the final result of extraction, the overflow region has grown to a considerable size; consequently, to remove the overflow region through a correction job is almost impossible. In order to minimize the deletion job, the overflow portion is deleted at a point of time when the expansion point enters the external region, namely, when the overflow region is not yet so large. After the overflow region is thus separated from the objective region, only the objective region can be extracted through the region expansion thereafter. (Reference is made to Article 3).
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