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

参考资料/NewGridAndCV/kappa.m

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+function KG = kappa(I)
+% get curvature information of input image
+% input: 2D image I
+% output: curvature matrix KG
+
+% Copyright (c) 2009, 
+% Yue Wu @ ECE Department, Tufts University
+% All Rights Reserved  
+
+I = double(I);
+[m,n] = size(I);
+P = padarray(I,[1,1],1,'pre');
+P = padarray(P,[1,1],1,'post');
+
+% central difference
+fy = P(3:end,2:n+1)-P(1:m,2:n+1);
+fx = P(2:m+1,3:end)-P(2:m+1,1:n);
+fyy = P(3:end,2:n+1)+P(1:m,2:n+1)-2*I;
+fxx = P(2:m+1,3:end)+P(2:m+1,1:n)-2*I;
+fxy = 0.25.*(P(3:end,3:end)-P(1:m,3:end)+P(3:end,1:n)-P(1:m,1:n));
+G = (fx.^2+fy.^2).^(0.5);
+K = (fxx.*fy.^2-2*fxy.*fx.*fy+fyy.*fx.^2)./((fx.^2+fy.^2+eps).^(1.5));
+KG = K.*G;
+KG(1,:) = eps;
+KG(end,:) = eps;
+KG(:,1) = eps;
+KG(:,end) = eps;
+KG = KG./max(max(abs(KG)));