feat: cDNA微阵列图像处理作业 - Python实现
实现内容: - 网格划分:投影分析 + 自相关估周期 + 白顶帽去背景 + 质心提取 - 三种阈值分割:人工阈值、Otsu自动阈值、迭代阈值 - TV去噪(Chambolle投影算法) - 后处理:去小连通域 + 保留最大连通域 - 完整可视化:网格叠加、阈值对比、收敛曲线、分割结果 参考MATLAB代码:NewGridAndCV/demo_GriddingAndCV.m
This commit is contained in:
@@ -0,0 +1,14 @@
|
||||
function H=Heaviside(z)
|
||||
% Heaviside step function (smoothed version)
|
||||
% Copyright (c) 2009,
|
||||
% Yue Wu @ ECE Department, Tufts University
|
||||
% All Rights Reserved
|
||||
|
||||
Epsilon=10^(-5);
|
||||
H=zeros(size(z,1),size(z,2));
|
||||
idx1=find(z>Epsilon);
|
||||
idx2=find(z<Epsilon & z>-Epsilon);
|
||||
H(idx1)=1;
|
||||
for i=1:length(idx2)
|
||||
H(idx2(i))=1/2*(1+z(idx2(i))/Epsilon+1/pi*sin(pi*z(idx2(i))/Epsilon));
|
||||
end;
|
||||
Reference in New Issue
Block a user