一种自适应的图像二值化

思想来源于：http://www.cnblogs.com/Imageshop/archive/2013/04/22/3036127.html

改进的策略是用一个2D高斯核去卷积图线，以此作为图像自适应的阈值

下面是对一组常见的MR心脏仿体图像实验的结果：

作者提供MATLAB:

% ADAPTIVETHRESH - Wellner's adaptive thresholding%% Thresholds an image using a threshold that is varied across the image relative% to the local mean, or median, at that point in the image.  Works quite well on% text with shadows%% Usage: bw = adaptivethresh(im, fsize, t, filterType, thresholdMode)%%        bw = adaptivethresh(im)  (uses default parameter values)%% Arguments:  im    - Image to be thresholded.%%             fsize - Filter size used to determine the local weighted mean%                     or local median.  %                     - If the filterType is 'gaussian' fsize specifies the%                     standard deviation of Gaussian smoothing to be%                     applied. %                     - If the filterType is 'median' fsize specifies the%                     size of the window over which the local median is%                     calculated.  %%                     The value for fsize should be large, around one tenth to%                     one twentieth of the image size.  It defaults to one%                     twentieth of the maximum image dimension.%%             t     - Depending on the value of 'mode' this is the value%                     expressed as a percentage or fixed amount, relative to%                     the local average, or median  grey value, below which%                     the local threshold is set. %                     Try values in the range -20 to +20.  %                     Use +ve values to threshold dark objects against a%                     white background.   Use -ve values if you are%                     thresholding white objects on a predominatly %                     dark background so that the local threshold is set%                     above the local mean/median. This parameter defaults to 15.%%    filterType     - Optional string specifying smoothing to be used%                     - 'gaussian' use Gaussian smoothing to obtain local%                     weighted mean as the  local reference value for setting%                     the local threshold. This is the default%                     - 'median' use median filtering to obtain local reference%                     value for setting the local threshold%%    thresholdMode  - Optional string specifying the way the threshold is%                     defined. %                     - 'relative' the value of t represents the percentage,%                     relative to the local average grey value, below which%                     the local threshold is set. This is the default.%                     - 'fixed' the value of t represents the fixed grey level%                     relative to the local average grey value, below which%                     the local threshold is set. %%                     Note that in the 'relative' threshold mode the amount the%                     threshold differs from the local mean/median will vary in%                     proportion with the local mean/median.  A small difference%                     from the local mean in the dark regions of the image will%                     be more significant than the same difference in a bright%                     portion of the image.  This will match with human%                     perception.  However this does mean that the results will%                     depend on the grey value origin and whether the image%                     is,say, negated.%% The implementation differs from Pierre Wellner's original adaptive% thresholding algorithm in that he calculated the local weighted mean just% along the row, or pairs of rows, in the image using a recursive filter.  Here% we use symmetrical 2D Gaussian smoothing to calculate the local mean.  This is% slower but more general.  This code also offers the option of using median% filtering as a robust alternative to the mean (outliers will not influence the% result) and offers the option of using a fixed threshold relative to the% mean/median.  Despite the potential advantage of median filtering being% more robust I find the output from using Gaussian filtering more pleasing.%% Reference: Pierre Wellner, "Adaptive Thresholding for the DigitalDesk" Rank% Xerox Technical Report EPC-1993-110  1993% Copyright (c) 2008 Peter Kovesi% School of Computer Science & Software Engineering% The University of Western Australia% pk at csse uwa edu au% http://www.csse.uwa.edu.au/% % Permission is hereby granted, free of charge, to any person obtaining a copy% of this software and associated documentation files (the "Software"), to deal% in the Software without restriction, subject to the following conditions:% % The above copyright notice and this permission notice shall be included in % all copies or substantial portions of the Software.%% The Software is provided "as is", without warranty of any kind.%% August 2008 function bw = adaptivethresh(im, fsize, t, filterType, thresholdMode)    % Set up default parameter values as needed    if nargin < 2fsize = fix(length(im)/20);    end            if nargin < 3t = 15;    end        if nargin < 4filterType = 'gaussian';    end            if nargin < 5thresholdMode = 'relative';    end        % Apply Gaussian or median smoothing    if strncmpi(filterType, 'gaussian', 3)g = fspecial('gaussian', 6*fsize, fsize);fim = filter2(g, im);    elseif strncmpi(filterType, 'median', 3)fim = medfilt2(im, [fsize fsize], 'symmetric');    elseerror('Filtertype must be ''gaussian'' or ''median'' ');    end        % Finally apply the threshold    if strncmpi(thresholdMode,'relative',3)bw = im > fim*(1-t/100);    elseif  strncmpi(thresholdMode,'fixed',3)bw = im > fim-t;    elseerror('mode must be ''relative'' or ''fixed'' ');    end