Ultrasound Image Texture Analysis

Multiple texture analysis methods for ultrasound images in DICOM format.

Citation

If you find this repository useful in your research, please consider citing the paper.

@misc{rezazadeh2022explainable,
      title={Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features}, 
      author={Alireza Rezazadeh and Yasamin Jafarian and Ali Kord},
      year={2022},
      eprint={2201.07227},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

Instructions

Texture Analysis for Ultrasound Images

Input:

   id (str): Subject ID  
   dir (str): directory of the DICOM input image file  
   issave (bool): save the figures and output data files?  
   savedir (str): save images as .fig in this directory  

Output:

     - Image:  
     
         . RawData: Raw pixel values  
         . ROI: Region of interes pixel values  
         . ROI_detrend: ROI with background trend correction  
         . Mask: Mask created to trim down ROI  
         . ROI_Masked: ROI after subtracting Mask  
         . ROI_Masked_Pixelcount: Number of pixels in Masked ROI 
         
     - Metrics:  
     
         . MovingAvgFilter: Filtered with a moving average window  
               .Stdev: Standard deviation filter  
               .Range: Range filter  
               .Entropy: Entropy filter  
               
         . FirstOrderStats: First-order surface analysis  
               .Skew_Biased:  
                      .Values: For all unmasked pixels  
                      .Average: Averaged across unmasked pixels  
               .Skew_unBiased:  
                      .Values  
                      .Average  
               .Kurtosis_Biased:  
                      .Values  
                      .Average  
               .Kurtosis_unBiased:  
                      .Values  
                      .Average  
               .Entropy:  
                      .Values  
                      .Average  
                      
         . SecondOrderStats: Second-order surface analysis  
               .autoc: Autocorrelation  
               .contr: Contrast  
               .corrm: Correlation: matlab  
               .corrp: Correlation  
               .cprom: Cluster Prominence  
               .cshad: Cluster Shade  
               .dissi: Dissimilarity  
               .energ: Energy: matlab  
               .entro: Entropy  
               .homom: Homogeneity: matlab  
               .homop: Homogeneity  
               .maxpr: Maximum probability  
               .sosvh: Sum of sqaures: Variance  
               .savgh: Sum average  
               .svarh: Sum variance  
               .senth: Sum entropy  
               .dvarh: Difference variance  
               .inf1h: Informaiton measure of correlation1  
               .inf2h: Informaiton measure of correlation2  
               .homom: Inverse difference (INV) is homom  
               .indnc: Inverse difference normalized (INN)  
               .idmnc: Inverse difference moment normalized  
               
         . GaborFilter: Gabor filter surface analysis  
               .W(alpha)O(theta): For Wavelength alpha Direction theta  
                      .Values: For all unmasked pixels  
                      .Average: Averaged across unmasked pixels  

Alireza Rezazadeh
rezaz003@umn.edu
Spring 2020