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Contents.m
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% The Breast Ultrasound Analysis Toolbox (BUSAT) contains 70 functions (m-files)
% to perfom image analysis including: preprocessing, segmentation, morphological
% and texture features, and binary classification (for two classes). This
% toolbox was developed with MATLAB R2014a.
%
% Developed by Wilfrido Gomez Flores ([email protected])
% Arturo Rodriguez Cristerna ([email protected])
% Wagner Coelho de Albuquerque Pereira ([email protected])
%
% Cinvestav-IPN (Mexico) - LUS/PEB/COPPE/UFRJ (Brazil)
%
% Please, cite the following paper where this toolbox was introduced:
%
% Arturo Rodriguez-Cristerna, Wilfrido Gomez-Flores, and Wagner Coelho de
% Albuquerque-Pereira, ''BUSAT: A MATLAB Toolbox for Breast Ultrasound Image
% Analysis'', In Proceedings: 9th Mexican Congress on Pattern Recognition
% (MCPR 2017), LNCS 10267, pp. 268-277, 2017.
%
%
% IMPORTANT: First run "RUN_ME_FIRST.m" file to add this toolbox to search path.
%
% Symbol "*" indicates that the function requires a compiled C code.
% Despite the compiled functions are provided for MACI64, PCWIN64, and
% GLNXA64, sometimes it is necessary to re-compile from the source code
% using the mex function. Please see the notes in the "help" of the
% respective m-functions to get more details about the source C codes.
%
%
% IMAGE PREPROCESSING
% -------------------
% CONTRAST ENHANCEMENT:
% clahe - Contrast-limited adaptive histogram equalization.
% fuzzyenh - Fuzzy enhancement.
% histequ - Histogram equalization.
% sace* - Adaptive contrast enhancement based on sigmoidal mapping function.
% sigmoidfilt - Sigmoid filter.
%
% DESPECKLING:
% adf - Anisotropic diffusion filtering.
% adlg - Anisotropic diffusion guided by Log-Gabor filters.
% chmf - Circular hybrid median filter.
% isf - Interference-based speckle filter.
% isfad - Interference-based speckle filter followed by anisotropic diffusion.
%
% DOMAIN TRANSFORMATION:
% multiloggabor - Multichannel decomposition by Log-Gabor filters.
% multiranklet* - Multichannel decomposition by ranklet transform.
% phasecong - Phase congruency of a gray-scale image.
% quantization - Gray-level quantization.
%
% LESION SEGMENTATION
% -------------------
% autosegment - Automatic segmentation of breast lesions using texture analysis.
% horsch - Semiautomatic segmentation of breast lesions with the Horsch's method.
% wtsdsegment - Semiautomatic segmentation of breast lesions using watershed transform.
%
% FEATURE EXTRACTION
% ------------------
% MORPHOLOGICAL:
% convhulldiff - Morphological features based on convex hull.
% equivellipse - Morphological features based on equivalent ellipse.
% fourierfactor - Fourier descriptors of lesion contour.
% fouriershape - Fourier-based shape features of a lesion.
% fractalcontour - Fractal dimension of the contour of a lesion.
% geometric - Geometric features of the lesion.
% margclass - Margin class features.
% nrl - Morphological features based on the normalized radial lenght curve.
% nspd_li - Number of protuberances and depressions and lobulation index.
% polymodel - Polygonal model of lesion contour.
% spiculation - Spiculation features of the lesion.
%
% TEXTURE:
% autocorr - Gray-level autocorrelation coefficient.
% autocov - Gray-level autocovariance coefficients.
% avmass - Gray-level average-based texture features.
% bdip_bvlc - BDIP and BVLC moments.
% bgc - Binary gradient contours.
% clxcurve - Complexity curve.
% fractaltexture - Fractal features of gray-level image.
% glcm - Gray-level co-occurrence matrix features.
% histfeatures - First order statistics from gray-level histogram.
% lawsenergy - Laws' texture energy measures.
% lbpv - Local binary pattern variance from phase congruency.
% nrg - Normalized radial gradient.
% pab - Posterior acoustic behavior.
%
% BI-RADS:
% birads_feats - BI-RADS features of US lexicon.
% bound_feats - Boundary features.
% echo_feats - Echo pattern features.
% margin_feats - Margin features.
% orient_feats - Orientation features.
% shape_feats - Shape features.
%
% CLASSIFICATION
% --------------
% CLASSIFIERS:
% bootstrap632AUC - AUC estimation using the .632+ bootstrap method.
% bootstrap632ERR - Classification error estimation using the .632+ bootstrap method.
% classifyLDA - Classify data with Fisher's linear discriminant analysis.
% classifyRBFN - Classify data with radial basis function network.
% classifySVM* - Classify data with non-linear support vector machine.
% classifyLSVM* - Classify data with linear support vector machine.
% classifyRF - Classify data with random forest classifier.
% classperf - Classification performance.
% trainLDA - Train Fisher's linear discriminant analysis classifier.
% trainRBFN - Train radial basis function network.
% trainSVM* - Train non-linear support vector machine classifier.
% trainLSVM* - Train linear support vector machine classifier.
% trainRF - Train random forest classifier.
%
% DATA NORMALIZATION:
% minmaxnorm - Minimum-Maximum normalization.
% softmaxnorm - Sigmoidal normalization.
% statnorm - Statistical normalization.
%
% FEATURE RANKING:
% featselect - Feature selection based on MRMR criterion.
% mrmr_corr - Minimal-redundancy-maximal-relevance (MRMR) criterion based on correlation.
% mrmr_mi - Minimal-redundancy-maximal-relevance (MRMR) criterion based on mutual information.
%
% MISCELLANEOUS
% -------------
% AUC - Area under the ROC curve.
% ROCAUC - Receiver operating characteristic curve.
% cropROI - Crop a region of interest.
% delintumor - Overlay tumor countour.
%
%
%