JP2006230910A5 - - Google Patents

Claims (35)

A first feature amount calculating means for calculating a feature amount representing a shape of a curved surface indicating a density distribution of a medical image;
Detecting means for detecting an abnormal shadow candidate region from the medical image using the feature amount calculated by the first feature amount calculating unit;
Second feature amount calculating means for calculating a feature amount of the abnormal shadow candidate region detected by the detecting means;
False positive deletion means for deleting false positives of the abnormal shadow candidate region based on the feature quantity calculated by the second feature quantity calculation means;
An image processing apparatus comprising:   The image processing apparatus according to claim 1, wherein the first feature amount calculating unit calculates a shape index as a feature amount representing the shape of the curved surface.   The image processing apparatus according to claim 1, wherein the feature quantity calculated by the second feature quantity calculation unit includes a shape index. The detection unit binarizes the medical image using a preset threshold value of the feature amount based on the feature amount calculated by the first feature amount calculation unit, so that an abnormal shadow candidate region is obtained. Detect
The image processing apparatus according to claim 1, wherein the false positive deletion unit deletes a false positive image region from the abnormal shadow candidate region obtained by the binarization.   The image processing apparatus according to claim 1, further comprising a display unit that displays an image obtained by deleting false positives by the false positive deleting unit. A setting unit for setting an arbitrary pixel of interest in the medical image and setting an image region within a predetermined range from the pixel of interest,
The image processing apparatus according to claim 1, wherein the first feature amount calculation unit calculates a feature amount representing a curved surface shape of a density distribution in the set image region. . The normal plane is rotated by a predetermined angle around the normal line of the pixel of interest on the curved surface showing the density distribution of the medical image as a rotation axis, and the curved surface of the image area within the predetermined range from the pixel of interest is plotted on the normal plane for each rotation angle. A function calculating means for calculating a function approximating a curve obtained by cutting out,
The image processing apparatus according to claim 1, wherein the first feature amount calculating unit calculates a feature amount based on the approximate function calculated by the function calculating unit.   The image processing apparatus according to claim 7, wherein the function calculating unit calculates a function that approximates the curve using a least square method. The function calculating means calculates an approximate circle as the approximate function,
The first feature amount calculation means calculates a curvature at the target pixel of the curve from the calculated approximate circle radius for each rotation angle, and calculates a feature amount based on the curvature calculated for each rotation angle. The image processing apparatus according to claim 7, wherein the image processing apparatus is an image processing apparatus.   The image processing apparatus according to claim 1, wherein the first feature amount calculating unit calculates feature amounts for all pixels of the medical image.   The said false positive deletion means deletes the image area | region outside a predetermined range as a false positive image area among the said abnormal shadow candidate area | regions, The Claim 1 characterized by the above-mentioned. Image processing apparatus.   The false positive deletion means deletes an image area whose shape is other than a predetermined shape from the abnormal shadow candidate areas as a false positive image area. Image processing apparatus.   The false positive deletion means uses various feature quantities of the medical image including the feature quantities calculated by the first and / or second feature quantity calculation means as input data, and indicates an index indicating the degree of true positive abnormal shadow The image processing apparatus according to claim 1, wherein false-positive image regions are deleted using multivariate analysis using values as output data.   The first feature amount calculation unit changes a region range of a curved surface from which a feature amount is calculated, and calculates a feature amount in the changed region range. An image processing apparatus according to 1.   The image processing according to any one of claims 1 to 13, wherein the first feature amount calculating unit changes a resolution of the medical image and calculates a feature amount with the changed resolution. apparatus.   The image processing apparatus according to claim 1, wherein an image area constituting a curved surface with a Gaussian distribution is detected as an abnormal shadow from the deletion of false positives by the false positive deletion unit. .   The image processing apparatus according to claim 16, wherein an image area of a tumor shadow is detected as an abnormal shadow from the false positive deletion by the false positive deletion means. A first feature amount calculating step of calculating a feature amount representing a shape of a curved surface indicating a density distribution of a medical image;
A detection step of detecting an abnormal shadow candidate region from the medical image using the feature amount calculated in the first feature amount calculation step;
A second feature amount calculating step of calculating a feature amount of the abnormal shadow candidate region detected in the detection step;
A false positive deletion step of deleting false positives of the abnormal shadow candidate region based on the feature amount calculated in the second feature amount calculation step;
An image processing method comprising:   The image processing method according to claim 18, wherein in the first feature amount calculating step, a shape index is calculated as a feature amount representing the shape of the curved surface.   20. The image processing method according to claim 18, wherein the feature quantity calculated in the second feature quantity calculation step includes a shape index. In the detection step, based on the feature amount calculated in the first feature amount calculation step, the medical image is binarized using a preset threshold value of the feature amount, whereby an abnormal shadow candidate region is obtained. Is detected,
The image processing method according to any one of claims 18 to 20, wherein in the false positive deletion step, a false positive image region is deleted from the abnormal shadow candidate region obtained by the binarization. .   The image processing method according to any one of claims 18 to 21, further comprising a display step of displaying an image obtained by deleting false positives in the false positive deletion step. A setting step of setting an arbitrary target pixel in the medical image and setting an image region within a predetermined range from the target pixel;
The image processing according to any one of claims 18 to 22, wherein, in the first feature amount calculation step, a feature amount representing a curved surface shape of a density distribution in the set image region is calculated. Method. The normal plane is rotated by a predetermined angle around the normal line of the pixel of interest on the curved surface showing the density distribution of the medical image as a rotation axis, and the curved surface of the image area within the predetermined range from the pixel of interest is plotted on the normal plane for each rotation angle. Including a function calculation step of calculating a function approximating a curve obtained by cutting,
The image processing method according to any one of claims 18 to 23, wherein in the first feature amount calculation step, a feature amount is calculated based on the approximate function calculated in the function calculation step. .   The image processing method according to claim 24, wherein in the function calculating step, a function that approximates the curve is calculated using a least square method. In the function calculating step, an approximate circle is calculated as the approximate function,
In the first feature amount calculation step, the curvature of the target pixel of the curve is calculated from the calculated radius of the approximate circle for each rotation angle, and the feature amount is calculated based on the curvature calculated for each rotation angle. The image processing method according to claim 24 or 25, wherein:   The image processing method according to any one of claims 18 to 26, wherein in the first feature amount calculation step, a feature amount is calculated for all pixels of the medical image.   28. The false positive deletion step, wherein, among the abnormal shadow candidate regions, an image region whose size is outside a predetermined range is deleted as a false positive image region. The image processing method as described.   28. The false positive deletion step, wherein an image region whose shape is other than a predetermined shape is deleted as a false positive image region among the abnormal shadow candidate regions. The image processing method as described.   In the false positive deletion step, various feature amounts of the medical image including the feature amount calculated in the first and / or second feature amount calculation step are used as input data, and an index indicating the degree of true positive abnormal shadow The image processing method according to any one of claims 18 to 29, wherein a false positive image region is deleted using multivariate analysis using a value as output data.   31. The method according to claim 18, wherein in the first feature amount calculation step, a region range of the curved surface for calculating the feature amount is changed, and the feature amount is calculated in the changed region range. The image processing method according to item.   The image according to any one of claims 18 to 30, wherein, in the first feature amount calculation step, the resolution of the medical image is changed, and the feature amount is calculated with the changed resolution. Processing method.   The image processing method according to any one of claims 18 to 32, wherein an image area constituting a curved surface with a Gaussian distribution is detected as an abnormal shadow from the false positive deletion in the false positive deletion step. .   34. The image processing method according to claim 33, wherein an image region of a tumor shadow is detected as an abnormal shadow from the false positive deletion in the false positive deletion step. Computer
A first feature amount calculating means for calculating a feature amount representing a shape of a curved surface indicating a density distribution of a medical image;
Detecting means for detecting an abnormal shadow candidate region from the medical image using the feature quantity calculated by the first feature quantity calculating means;
A second feature amount calculating means for calculating a feature amount of the abnormal shadow candidate region detected by the detecting means;
False positive deletion means for deleting false positives of the abnormal shadow candidate region based on the feature quantity calculated by the second feature quantity calculation means;
Program to function as.