Algorithm 1 New Seed Detection Algorithm
Input: Icolor: original image; L ← Ø; V(x) ← 0, ∀x; parameters (ρ(r), {si}, α, β, D1, D2)
Output: L: a list of seeds for cells
1:	{Initialization Phase}
2:	Decouple Icolor into two stain channels: hematoxylin IH and eosin IE
3:	Construct mask image: Ψ ← complement(IH) and marker image: Φ ← IH ⊕ ρ
4:	Compute reconstructed image ${\mathbf{R}}_{\mathrm{\Phi }}^{{\chi }_{\rho }}(\mathrm{\Psi })\leftarrow \text{morphRecon}(\mathrm{\Phi },\mathrm{\Psi },\rho )$
5:	Compute difference image $\mathrm{\Delta }\leftarrow \mathrm{\Psi }-{\mathbf{R}}_{\mathrm{\Phi }}^{{\chi }_{\rho }}(\mathrm{\Psi })$
6:	Igray(x) ← rgb2Gray(Icolor); M(x) ← otusuThresholding(Igray(x))
7:	{Voting Map Construction}
8:	for all i ∈ (1, 2, …, N) do
9:	for all x ∈ Ω do
10:	λ2 (x) ← eigHessian(Δ(x), si)
11:	if sign(λ2(x))< 0 then
12:	V(x) ← V(x) + 1
13:	{Detecting and Merging Seeds}
14:	vsort ← descentSort(V(x)); v0→1 ← flip(normalize(vsort))
15:	Let vc and vp be the current and previous voting value in vsort
16:	for all vc and vp ∈ vsort, where vc ← vsort(i) do
17:	find objects 𝒪(vc) ← label(V(x) ≥ vc)
18:	for all o ∈ 𝒪(vc) do
19:	if size(o)≥ A(α, β, vi) = β + exp(αv0→1 (i)) && M(centroid(o)) = 1 && o ∩ L = Ø then
20:	L ← L ∪ {centroid(o)}
21:	while any(L.pairwiseDistance() ≤ D1)=true do
22:	find p ∈ L s.t. any(L.pairwiseDistance(p) ≤ D1) = true && sum(L.pairwiseDistance(p)) ≤ sum(L.pairwiseDistance(p′)) ∀p′ ∈ L
23:	find Q = {q| s.t. (L.pairwiseDistance(p, q) ≤ D1) = true}
24:	L ← L ∪ Mean({p} ∪ Q)
25:	L ← L \ ({p} ∪ Q)
26:	{Merging Seeds with Edge Information}
27:	while any(L.pairwiseDistance() ≤ D2 && !L.isBlockedByEdge()) = true do
28:	for all p ∈ L do
29:	find Q(p) = {q| s.t. (L.pairwiseDistance(p, q) ≤ D2 && !L.isBlockedByEdge(p, q)) = true}
30:	find p ∈ L s.t. Q(p) ∆ Ø && sum(L.pairwiseDistance(p, Q(p))) ≤ sum(L.pairwiseDistance(p′,Q(p′))) ∀p′ ∈ L
31:	L ← L ∪ Mean({p} ∪ Q(p))
32:	L ← L \ ({p} ∪ Q(p))