【图像分割】基于matlab GUI二值化+灰白质医学影像分割【含Matlab源码 184期】

一、图像分割简介

理论知识参考：【基础教程】基于matlab图像处理图像分割【含Matlab源码 191期】

二、部分源代码

function varargout = brain_ysw(varargin)
% BRAIN_YSW MATLAB code for brain_ysw.fig
%      BRAIN_YSW, by itself, creates a new BRAIN_YSW or raises the existing
%      singleton*.
%
%      H = BRAIN_YSW returns the handle to a new BRAIN_YSW or the handle to
%      the existing singleton*.
%
%      BRAIN_YSW('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in BRAIN_YSW.M with the given input arguments.
%
%      BRAIN_YSW('Property','Value',...) creates a new BRAIN_YSW or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before brain_ysw_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to brain_ysw_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help brain_ysw

% Last Modified by GUIDE v2.5 10-Jun-2015 13:34:14

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @brain_ysw_OpeningFcn, ...
                   'gui_OutputFcn',  @brain_ysw_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT

  

 

  
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三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 蔡利梅.MATLAB图像处理——理论、算法与实例分析[M].清华大学出版社，2020.
[2]杨丹,赵海滨,龙哲.MATLAB图像处理实例详解[M].清华大学出版社，2013.
[3]周品.MATLAB图像处理与图形用户界面设计[M].清华大学出版社，2013.
[4]刘成龙.精通MATLAB图像处理[M].清华大学出版社，2015.
[5]赵勇,方宗德,庞辉,王侃伟.基于量子粒子群优化算法的最小交叉熵多阈值图像分割[J].计算机应用研究. 2008,(04)

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