Image segmentation method
First Claim
1. A method of classifying grey scale image data according to different region types within the image, the method comprising:
- assigning a single feature value to each datum;
reducing the resolution of each value of the data;
generating a histogram of the reduced resolution value data; and
, performing a fast fuzzy c-means clustering algorithm on all of the histogram data, so as to minimize a fuzzy object function Jm(W, v), wherein Jm(W, v) is defined by the equation
using generated values f(g) of said reduced resolution histogram, and wherein;
c represents a number of clusters in the image;
Gmin and Gmax represent respectively a minimum and maximum grey scale value q in the image;
m is a fuzzy weighting exponent;
w is a fuzzy partition of the histogram data with entries wig which correspond to a membership value of each grey scale value g in a cluster i of the image; and
, dig is a Euclidian distance between a grey scale value q and v1.
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Abstract
A method of producing a simulated 3-dimensional image, the method comprising: assimilating 2-dimensional image data, e.g. from MR image slices; assigning a feature, e.g. gray scale value to each datum; reducing the resolution of each gray scale value of the data; generating a histogram of the reduced resolution gray scale data; performing a fast fuzzy c-means clustering on the histogram data. The resolution of each pixel object in the images may be reduced from 12 bit to 8 bit resolution, and the histogram may be generated from these. Subsequently a value may be assigned for each entry in the histogram, each entry value being equal to the number of objects of any given feature (e.g. gray scale) in the reduced resolution (8 bit) image. The image may be displayed using a novel color blending technique. A 3D image is produced quickly and without supervisory intervention and can be used in endoscopic surgery and in diagnostic methods as well as in understanding healthy anatomical features better.
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Citations
9 Claims
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1. A method of classifying grey scale image data according to different region types within the image, the method comprising:
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assigning a single feature value to each datum;
reducing the resolution of each value of the data;
generating a histogram of the reduced resolution value data; and
,performing a fast fuzzy c-means clustering algorithm on all of the histogram data, so as to minimize a fuzzy object function Jm(W, v), wherein Jm(W, v) is defined by the equation
using generated values f(g) of said reduced resolution histogram, and wherein;
c represents a number of clusters in the image;
Gmin and Gmax represent respectively a minimum and maximum grey scale value q in the image;
m is a fuzzy weighting exponent;
w is a fuzzy partition of the histogram data with entries wig which correspond to a membership value of each grey scale value g in a cluster i of the image; and
,dig is a Euclidian distance between a grey scale value q and v1. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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Specification