Image analysis – Applications – Biomedical applications
Reexamination Certificate
2000-09-14
2004-03-09
Chang, Jon (Department: 2623)
Image analysis
Applications
Biomedical applications
C382S180000, C382S194000, C382S224000
Reexamination Certificate
active
06704439
ABSTRACT:
CROSS-REFERENCE TO RELATED APPLICATIONS
Applicants claim priority under 35 U.S.C. §119 from Korean Patent Application No. 1999-39328, filed Sep. 14, 1999, and entitled “Method for Identification of Alveolar Never Region in Mandible Region.”
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a method for identifying an alveolar nerve region in a mandible image during dental implantation, and more particularly, to an automated or semi-automated method for identifying an alveolar nerve region in a mandible image obtained through computed tomography (CT).
2. Description of the Related Art
For cases where damage to teeth is too serious to repair, surgery for substituting artificial teeth for damaged teeth has become common. For such surgery, an implant screw for supporting the artificial teeth must be inserted into the jawbone.
FIGS. 1A through 1G
illustrate each step of implantation. In detail, in the case where a tooth is extracted due to damage as shown in
FIG. 1A
, an artificial tooth is implanted into the damaged region as follows. The gum in the damaged region is cut as shown in
FIG. 1B
, a region into which an implant screw is to be inserted is drilled to form a hole as shown in
FIG. 1C
, and the implant screw is inserted into the hole as shown in FIG.
1
D. Then, the implanted region is left to allow the implant screw to firmly bind with the jawbone and new tissue to cover the implant screw, as shown in FIG.
1
E. When the implant screw has firmly bound to the jawbone, the gum on the top of the implanted region is removed as shown in FIG.
1
F and then an artificial tooth is mounted on the implant screw.
However, if a dentist fails to insert the implant screw into an appropriate region in an accurate direction during the above surgery, the implant screw cannot satisfactorily support the artificial tooth or the inappropriately inserted implant screw may encroach on alveolar nerves, causing facial paralysis. Thus, the most important step in implantation is to accurately assess the density of the jawbone in the vicinity of a desired implantation site. In particular, contact between the implant screw and a low-density area and encroachment on the nerves in the jawbone must be avoided. Thus, it is important for a dental surgeon to inspect accurately the location of the nerves near a desired implant site.
Success in implantation depends on how accurately a dental surgeon knows the jawbone density of a patient. The current leading method in accurately ascertaining the jawbone density is computed tomography (CT). CT refers to when an object is scanned in many directions to acquire a 3-dimensional image of the object. At a dental surgery, during CT scanning, either the maxilla or the mandible is typically scanned in 1.0-mm increments, resulting in about 45 image slices.
FIG. 2
shows an image of a mandible obtained by CT. As shown in
FIG. 2
, the CT scanning and computer simulation technique provide a large amount of information on the mandible to a dental surgeon. However, it is not easy for the dental surgeon to directly and accurately identify the location of the nerves from the CT images.
SUMMARY OF THE INVENTION
To solve the above problems, it is an objective of the present invention to provide an automated method for identifying an alveolar nerve region in a mandible image obtained by computed tomography (CT).
It is another objective of the present invention to provide a method for identifying an alveolar nerve region from a mandible image obtained by CT, using a seed point present in a CT slice.
It is another objective of the present invention to provide computer readable media for the alveolar nerve identification methods.
According to an aspect of the present invention, there is provided a method of identifying an alveolar nerve region in a mandible image, comprising the steps of: (a) slicing the 3-dimensional mandible image into a number of 2-dimensional slice images; (b) detecting a binary image object corresponding to a mandible region from one of the slice images; (c) grouping pixels of the binary image object of the mandible region into clusters each containing pixels having a similar intensity; (d) determining clusters that have pixels more than a predetermined minimum number of pixels, and determining the minimum labeled cluster having the lowest pixel intensity distribution among the clusters; (e) composing a new binary image containing pixels which belong to both the mandible region and the clusters having intensity distribution lower than that of the minimum labeled cluster, to extract a candidate nerve object; and (f) determining whether the candidate nerve object corresponds to the real alveolar nerve region.
The alveolar nerve region identification method may further comprise determining candidate nerve objects for the real alveolar nerve region with respect to all of the slice image, and assembling all of the slice images into a mandible image to produce a complete alveolar nerve region in the mandible image using the candidate nerve objects. Also, the method may further comprise identifying an alveolar nerve region for a neighboring slice image S
i−1
or S
i+1
, which is located before or after the slice image S
i
by growing the alveolar nerve region determined from the slice S
i
.
Preferably, step (f) comprises: (f1) performing a dilation operation on the candidate nerve object to extract the perimeter region thereof; (f2) comparing the intensity of the pixels belonging to the perimeter region with the intensity of the inner pixels surrounded by the perimeter region; and (f3) determining an object having a perimeter region whose pixel intensity is greater than that of the inner pixels, as a new candidate nerve object. Alternatively, step (f) may comprise: (f1) calculating the number N
1
of pixels belonging to the candidate nerve object; (f2) calculating the centroid point of the candidate nerve object; (f3) performing a region growing operation on the candidate nerve object, starting from the centroid point as a seed point to produce a grown nerve object; (f4) calculating the number N
2
of pixels of the grown nerve object; and (f5) comparing N
1
and N
2
, and if N
2
is greater than N
1
by a predetermined number or more, removing the candidate nerve object. In another embodiment, step (f) may comprise: (f1) calculating a centroid point with respect to all of the pixels belonging to the mandible region of the slice image; (f2) determining the uppermost and lowermost pixels of the mandible region, and calculating a halfway point between the uppermost and lowermost pixels; (f3) determining whether the centroid point is located near the halfway point; and (f4) if the centroid point is located near the halfway point, determining the candidate nerve object above the halfway point and nearest to the centroid point, or the candidate nerve object below and nearest to the centroid point, to be a real alveolar nerve region, and if the centroid point is not near the halfway point, determining the candidate nerve object nearest to the centroid point to be an alveolar nerve region.
According to another aspect of the present invention, there is provided a method of identifying an alveolar nerve region in a mandible image, comprising the steps: (a) slicing the 3-dimensional mandible image into a number of 2-dimensional slice images, and selecting one of the slice images; (b) displaying candidate nerve pixels of the selected slice image, one of the candidate nerve pixels being selected as a seed point to be used in identifying the alveolar nerve region of the slice image; (c) a user determining the seed point among the candidate nerve pixels; and (d) comparing the intensity of the seed point with the intensity of neighboring pixels, and performing a region growing process on the slice image based on whether a difference between the compared intensities is within a predetermined error range, to detect pixels corresponding to an alveolar nerve region of the slice image.
Preferably, the alveolar nerve identification meth
Kong Chang-hwan
Lee Chan-kyung
Lee Heu-yeon
10DR Co. Ltd.
Chang Jon
Kim Charles
Knobbe Martens Olson & Bear LLP
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