let infection = parasites.infection.Copy() // To avoid to modify the parameter.
// This is the minimum window size to check if other ellipses touch 'e'.
- let searchRegion (e: Ellipse) = { KdTree.minX = e.Cx - (e.A + config.RBCMaxRadius)
- KdTree.maxX = e.Cx + (e.A + config.RBCMaxRadius)
- KdTree.minY = e.Cy - (e.A + config.RBCMaxRadius)
- KdTree.maxY = e.Cy + (e.A + config.RBCMaxRadius) }
+ let searchRegion (e: Ellipse) = { KdTree.minX = e.Cx - (e.A + config.RBCRadius.Max)
+ KdTree.maxX = e.Cx + (e.A + config.RBCRadius.Max)
+ KdTree.minY = e.Cy - (e.A + config.RBCRadius.Max)
+ KdTree.maxY = e.Cy + (e.A + config.RBCRadius.Max) }
// The minimum window to contain a given ellipse.
let ellipseWindow (e: Ellipse) =
// We reverse the list to get the lower score ellipses first.
let ellipsesWithNeigbors = ellipses |> List.map (fun e -> e, neighbors e) |> List.rev
-
// 2) Remove ellipses touching the edges.
for e in ellipses do
if e.isOutside w_f h_f then e.Removed <- true
if stdDeviation > globalStdDeviation * config.Parameters.standardDeviationMaxRatio then
e.Removed <- true
-(*
- let imgData = img.Data
- let stdDeviations = [
- for e in ellipses do
- if not e.Removed
- then
- let shrinkedE = e.Scale 0.9f
- let minX, minY, maxX, maxY = ellipseWindow shrinkedE
-
- let stdDeviation = float32 <| MathNet.Numerics.Statistics.Statistics.StandardDeviation (seq {
- for y in (if minY < 0 then 0 else minY) .. (if maxY >= h then h - 1 else maxY) do
- for x in (if minX < 0 then 0 else minX) .. (if maxX >= w then w - 1 else maxX) do
- if shrinkedE.Contains (float32 x) (float32 y)
- then
- yield float imgData.[y, x, 0] })
-
- e.StdDeviation <- stdDeviation
- yield stdDeviation ]
-
- // We use Otsu and eliminate some cells only if the curve may be bimodal.
- // See https://en.wikipedia.org/wiki/Multimodal_distribution#Bimodality_coefficient
- let skewness, kurtosis = MathNet.Numerics.Statistics.Statistics.PopulationSkewnessKurtosis (stdDeviations |> List.map float)
- let n = float stdDeviations.Length
- let bimodalityCoefficient = (skewness ** 2. + 1.) / (kurtosis + 3. * (n - 1.) ** 2. / ((n - 2.) * (n - 3.)))
-
- if bimodalityCoefficient > 5. / 9.
- then
- let hist = ImgTools.histogram stdDeviations 200
- let thresh, _, _ = ImgTools.otsu hist
- for e in ellipses do
- if not e.Removed && e.StdDeviation > thresh
- then e.Removed <- true
-*)
// 4) Remove ellipses with little area.
- let minArea = config.RBCMinArea
+ let minArea = config.RBCRadius.MinArea
for e, neighbors in ellipsesWithNeigbors do
if not e.Removed
then