Systems and methods for diagnosing tumors in a subject by performing a quantitative analysis of texture-based features of a tumor object in a radiological image

Abstract

An example method for diagnosing tumors in a subject by performing a quantitative analysis of a radiological image can include identifying a region of interest (ROI) in the radiological image, segmenting the ROI from the radiological image, identifying a tumor object in the segmented ROI and segmenting the tumor object from the segmented ROI. The method can also include extracting a plurality of quantitative features describing the segmented tumor object, and classifying the tumor object based on the extracted quantitative features. The quantitative features can include one or more texture-based features.

1 .- 22 . (canceled) 
     
     
         23 . A method for diagnosing tumors in a subject by performing a quantitative analysis of a radiological image, comprising:
 identifying a region of interest (ROI) in the radiological image;   segmenting the ROI from the radiological image;   identifying a tumor object in the segmented ROI;   segmenting the tumor object from the segmented ROI;   extracting a plurality of quantitative features describing the segmented tumor object; and   classifying the tumor object based on the extracted quantitative features, wherein the quantitative features include one or more texture-based features.   
     
     
         24 . The method of  claim 23 , wherein classifying the tumor object based on the extracted quantitative features further comprises predicting whether the tumor object is a malignant or benign tumor. 
     
     
         25 . The method of  claim 23 , wherein classifying the tumor object based on the extracted quantitative features further comprises using a decision tree algorithm, a nearest neighbor algorithm or a support vector machine. 
     
     
         26 . The method of  claim 23 , wherein each of the texture-based features describes a spatial arrangement of image intensities within the tumor object. 
     
     
         27 . The method of  claim 23 , wherein the texture-based features include at least one of a run-length texture feature, a co-occurrence texture feature, a Laws texture feature, a wavelet texture feature or a histogram texture feature. 
     
     
         28 . The method of  claim 23 , wherein the quantitative features include one or more shape-based features. 
     
     
         29 . The method of  claim 28 , wherein each of the shape-based features describes a location, a geometric shape, a volume, a surface area, a surface-area-to-volume ratio or a compactness of the tumor object. 
     
     
         30 . The method of  claim 23 , wherein a total number of quantitative features is greater than approximately 200. 
     
     
         31 . The method of  claim 30 , wherein a number of texture-based features is greater than approximately 150. 
     
     
         32 . The method of  claim 23 , wherein the ROI is a lung field. 
     
     
         33 . The method of  claim 23 , wherein the radiological image is a low-dose computed tomography (CT) image. 
     
     
         34 . The method of  claim 23 , further comprising:
 reducing the extracted quantitative features to a subset of extracted quantitative features; and   classifying the tumor object based on the subset of extracted quantitative features.   
     
     
         35 . The method of  claim 34 , wherein the subset of extracted quantitative features includes one or more quantitative features that are predictive of the classification of the tumor object. 
     
     
         36 . The method of  claim 35 , further comprising determining the one or more quantitative features that are predictive of the classification of the tumor object using at least one of a Recursive Elimination of Features (Relief-F) algorithm, a Correlation-Based Feature Subset Selection for Machine Learning (CFS) algorithm or a Relief-F with Correlation Detection algorithm. 
     
     
         37 . The method of  claim 34 , wherein the subset of extracted quantitative features includes one or more non-redundant quantitative features having adequate reproducibility and dynamic range. 
     
     
         38 . The method of  claim 37 , wherein reducing the extracted quantitative features to a subset of extracted quantitative features further comprises eliminating one or more of the extracted quantitative features having a reproducibility metric between a baseline radiological image and a subsequent radiological image less than a predetermined reproducibility value, the subsequent radiological image being captured a fixed period of time after the baseline radiological image was captured. 
     
     
         39 . The method of  claim 38 , wherein the reproducibility metric is a concordance correlation coefficient. 
     
     
         40 . The method of  claim 38 , wherein the predetermined reproducibility value is less than 0.90. 
     
     
         41 . The method of  claim 38 , wherein reducing the extracted quantitative features to a subset of extracted quantitative features further comprises eliminating one or more of the extracted quantitative features having a dynamic range less than a predetermined dynamic range value. 
     
     
         42 . The method of  claim 38 , wherein reducing the extracted quantitative features to a subset of extracted quantitative features further comprises eliminating one or more of the extracted quantitative features that are redundant quantitative features.