Image analysis – Applications – Biomedical applications
Reexamination Certificate
1998-09-22
2002-07-09
Mancuso, Joseph (Department: 2721)
Image analysis
Applications
Biomedical applications
C382S128000
Reexamination Certificate
active
06418238
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates to an image detection apparatus for detecting a roundish shape, specifically a rounded convex region such as a malignant tumor, from an image of part of a human body photographed by X-ray photography and a detection method thereof.
A technique for recognizing a malignant tumor from an image taken by, for example, X-ray photography is normally executed with the following two-stage processing. In the first stage,.candidate malignant tumor points are extracted by filter processing. In the second stage, coupling processing is carried out and then normal regions which have been false-positive are deleted with the candidate malignant tumor remaining.
The first conventional technique for realizing those first and second stages will be described.
As a filter for extracting a malignant tumor in the first stage, a Min-DD filter for outputting a minimum value from second order directional differential filter outputs in all directions is known.
The Min-DD filter has an excellent function of controlling image regions of vein regions such as blood vessels and relatively emphasizing a malignant tumor of the type which is constituted only of a pixel intensity surface in which gradients change relatively greatly in all directions. A pixel intensity surface is a graph in which an image is positioned in the x-plane and the y-plane and the pixel intensity is plotted in the z-axis direction. Refer to Systems and Computers in Japan, Vol. 26, No. 11, pp. 38-51, 1995 for more detail.
Several methods for utilizing a neural network which classifies candidate malignant tumor region or images into malignancy and benignancy have been proposed. One of those methods is disclosed in detail in, for example, U.S. Pat. No. 5,463,548 (1995) by N. Asada and K. Doi.
As a method for deleting normal regions which have been false-positive in the second stage, the analysis of two-dimensional shapes including, for example, areas and roundness is carried out. In addition, regions are narrowed down using evaluation measures, such as the standard deviation of pixel intensity values within the regions and the contrast of the regions with their surroundings with reference back to the original image. This is disclosed in, for example, U.S. Pat. No. 5,579,360; 1996 by Mohamed. Abdel-Mottaleb in detail.
The aforementioned Min-DD filter functions to curb mammary glands and blood vessels while maintaining a malignant tumor of the type which consists only of a pixel intensity curved surface in which gradient changes are relatively great in all directions. At the same time, however, the filter curbs the pixel intensity curved surface even within a malignant tumor which has slight gradient changes in any directions. There exist, in fact, malignant tumors which have a pixel intensity curved surface in which gradient changes are slight in all directions.
Since the Min-DD filter curbs such portions, defects sometime occur at the time candidate malignant tumor points are extracted.
Moreover, it has never been proposed that a method using a neural network not for determination of the candidate regions but for determination of candidate points. In use of a neural network for the determination of candidate points, if the same teaching signals are given to the surrounding portion of a malignant tumor and points within the tumor and a different teaching signal is given to points which constitute the vein region, such as a blood vessel, then the efficiency of discriminating the constitutional points of the malignant tumor from those of the vein region such as a blood vessel decreases.
Furthermore, the candidate malignant tumor points extracted by the filter processing in the fist stage are extracted because they have similar features to those of the points constituting the malignant tumor. It has to be determined whether or not the pixel intensity curved surfaces in the coupled regions formed by those points are similar to the three-dimensional shape of the malignant tumor region.
In the conventional techniques as described above, the analysis of two-dimensional shape of the coupled region obtained as a binary value image and the statistical analysis of average pixel intensity values while referring back to the original image are carried out. However, the detailed examination of balances in the corresponding region using the differential information on the pixel intensity curved surface for purposes of analyzing the three-dimensional shape is not at all seen.
In case the points thus extracted form a small region, the difference between a circle and a rectangular shape peculiar to two-dimensional digital image processing is only slight and measures for evaluating two-dimensional shapes are not very effective.
This follows that it is required for a determination standard for a small region to be based on the three-dimensional shape on the pixel intensity curved surface.
Next, the second conventional technique for realizing the aforementioned first and second stages will be described.
First, first stage processing will be described. As a filter for extracting candidate malignant tumor points, there has been proposed an Iris filter which calculates the degree of the convergence of gradient vectors. The iris filter is excellent in extracting a rounded convex region, which content is disclosed in Proc. of the Int. Conf. on Image Processing, Vol. I, pp. 407-410, 1994. The degree of the convergence of gradient vectors is calculated through the following processing.
Using arrangements f
1
to f
16
of pixel intensity values as illustrated by
FIG. 11
, the gradient vector direction &thgr; is calculated. The calculation is based on the following mathematical expression:
θ
=
tan
-
1
(
f
3
+
f
4
+
f
5
+
f
6
+
f
7
)
-
(
f
11
+
f
12
+
f
13
+
f
14
+
f
15
)
(
f
1
+
f
2
+
f
3
+
f
15
+
f
16
)
-
(
f
7
+
f
8
+
f
9
+
f
10
+
f
11
)
Next, the convergence C of gradient vectors is calculated using the following mathematical expression:
C
=
(
1
/
N
)
∑
j
=
1
N
cos
θ
j
where symbol N denotes the total number of pixels within a circle with a radius of R.
Specifically, if a vector, having a noted point as a starting point and an arbitrary point a within neighboring regions as an end point, is defined as A and a vector indicating the gradient direction at the point a as G, then a cosine value of an angle made between the vectors A and G is calculated for every point within the neighboring regions and one point is obtained as their average. The Iris filter is characterized in that it does use only directions of gradient vectors and not magnitudes thereof and therefore does not depend on contrast. It is also characteristically designed such that filter size is variable appropriately in accordance with the magnitude of the malignant tumor.
Second stage processing will next be described. As regards the conventional method in which, after coupling processing has been carried out, normal regions (pseudo-malignant tumors) which have been false-positive are deleted with the malignant tumor remaining, there have been proposed an deletion method. In the deletion method, statistics on all pixels within regions, such as the average or variance of pixel intensity values within the regions, is taken into consideration. Alternatively, roundness of the two-dimensional shape is taken into consideration.
However, if applying the Iris filter stated above to, for example, mammography, the output of the neighboring regions of the malignant tumor tends to be high and the output of the shadow portion near the straight line of, for example, a mammary gland also tends to be relatively high. Due to this, if those pixels whose output values of the Iris filter are equal to or higher than a certain threshold value are picked up and all malignant tumor regions are intended to be extracted completely, then even the shadow portion near the straight line of, for example, a mammary gland is also false-positive.
Even if the degree of the convergence of gradient vectors used in the Iris
Shiratani Fumiyuki
Terashima Mikihiko
Frishauf Holtz Goodman & Chick P.C.
Mancuso Joseph
Olympus Optical Co,. Ltd.
Tabatabai Abolfazl
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