Method and apparatus for analyzing CT images to determine the presence of pulmonary tissue pathology

A method for automated analysis of a diagnostic medical image, said image comprising a plurality of pixels, with each pixel having a particular gray level assigned thereto, said method comprising:

• defining a region of interest (ROI) on said image;
• forming a co-occurrence matrix within said ROI, said forming including:
• selecting a pixel “x” within said ROI;
• determining the gray level “i” of pixel “x”;
• and
• determining a gray level “j” of a pixel residing at a distance “d” in a direction “φ” relative to pixel “x,” wherein distance “d” is approximately two pixels in length;
• forming pixel regions within said image by assigning a common gray level to a group of pixels which possess substantially similar gray levels;
• obtaining measures from said image which describe relationships between said pixels, said obtaining including obtaining at least one co-occurrence matrix measure within said ROI to describe spatial interdependencies between the pixels of said ROI;
• and
• classifying areas of said image to a particular tissue pathology class based upon said texture measures obtained.

An apparatus, comprising:

• an image input adapted to receive a diagnostic medical image, said image comprising a plurality of pixels, with each pixel having a particular gray level assigned thereto;
• and
• a processor adapted to define a region of interest (ROI) on said image and to form a co-occurrence matrix within said ROI by at least:
• selecting a pixel “x” within said ROI;
• determining the determining the gray level “i” of pixel “x”;
• and
• determining a gray level “j” of a pixel residing at a distance “d” in a direction “φ” relative to pixel “x,” wherein distance “d” is approximately two pixels in length;
• said processor further adapted to perform texture measures on a group of pixels within said image, said texture measures providing information on an occurence frequency of gray levels assigned to said group of pixels and spatial interdependencies between particular pixels of said group of pixels, said texture measures including at least one co-occurrence matrix measure within said ROI to describe spatial interdependencies between the pixels of said ROI, said processor being adapted to classify said group of pixels to a tissue pathology class based upon said texture measures obtained, said processor being further adapted to form pixel regions within said image, wherein said pixels that are located adjacent one another are assigned a common gray level providing that said adjacent pixels' gray levels differ by an insignificant amount.

A method for automated analysis of textural differences present on a diagnostic medical image of the pulmonary region, said image comprising a plurality of pixels, with each pixel having a gray level assigned thereto, said method comprising:

• defining a region of interest (ROI) on said image;
• forming pixel regions within said image by assigning a common gray level to a group of pixels which possess substantially similar gray levels;
• performing at least one first order texture measure within said ROI to describe a frequency of occurrence of all gray levels assigned to pixels of said ROI;
• determining the number of times a particular gray level “i” of a pixel “x” within said ROI and a particular gray level “j” of another pixel within said ROI are separated by a distance of approximately two pixels in a direction “φ” relative to pixel “x”;
• performing at least one second order texture measure within said ROI to describe spatial interdependencies between the pixels of said ROI;
• centering a pixel block about each pixel of said ROI;
• determining the average of absolute gray level intensity differences of each possible pixel-pair separated by a distance “d” within said pixel block;
• assigning the pixel, about which said pixel block is centered, a stochastic fractal value based upon said average absolute gray level intensity differences obtained;
• and
• classifying said ROI as belonging to a tissue pathology class based upon said first and second order texture measures and said stochastic fractal value obtained.