module Classifier
open System
+open System.Collections.Generic
open System.Drawing
open Emgu.CV
open Emgu.CV.Structure
+open Types
+open Utils
-type CellClass = HealthyRBC | InfectedRBC | Peculiar
-type Cell = {
- cellClass: CellClass
- center: Point
- elements: Matrix<byte> }
+type private EllipseFlaggedKd (e: Ellipse) =
+ inherit Ellipse (e.Cx, e.Cy, e.A, e.B, e.Alpha)
-type KdEllipse (e: Types.Ellipse) =
- inherit Types.Ellipse (e.Cx, e.Cy, e.A, e.B, e.Alpha)
-
- interface KdTree.I2DCoords with
+ member val Removed = false with get, set
+
+ interface KdTree.I2DCoords with
member this.X = this.Cx
member this.Y = this.Cy
-
-
-let findCells (ellipses: Types.Ellipse list) (parasites: ParasitesMarker.Result) (fg: Image<Gray, byte>) : Cell list =
+
+
+let findCells (ellipses: Ellipse list) (parasites: ParasitesMarker.Result) (img: Image<Gray, float32>) (config: Config.Config) : Cell list =
if ellipses.IsEmpty
then
[]
else
- let tree = KdTree.Tree.buildTree (ellipses |> List.map KdEllipse)
- []
\ No newline at end of file
+ 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) }
+
+ // The minimum window to contain a given ellipse.
+ let ellipseWindow (e: Ellipse) =
+ let cx, cy = roundInt e.Cx, roundInt e.Cy
+ let a = int (e.A + 0.5f)
+ cx - a, cy - a, cx + a, cy + a
+
+ let w = img.Width
+ let w_f = float32 w
+ let h = img.Height
+ let h_f = float32 h
+
+ // Return 'true' if the point 'p' is owned by e.
+ // The lines represents all intersections with other ellipses.
+ let pixelOwnedByE (p: PointD) (e: Ellipse) (others: (Ellipse * Line) list) =
+ e.Contains p.X p.Y &&
+ seq {
+ let c = PointD(e.Cx, e.Cy)
+ for e', d1 in others do
+ let d2 = Utils.lineFromTwoPoints c p
+ let c' = PointD(e'.Cx, e'.Cy)
+ let v = pointFromTwoLines d1 (lineFromTwoPoints c c')
+ let case1 = sign (v.X - c.X) <> sign (v.X - c'.X) || Utils.squaredDistanceTwoPoints v c > Utils.squaredDistanceTwoPoints v c'
+ if d2.Valid
+ then
+ let p' = Utils.pointFromTwoLines d1 d2
+ // Yield 'false' when the point is owned by another ellipse.
+ if case1
+ then
+ yield sign (c.X - p.X) <> sign (c.X - p'.X) || Utils.squaredDistanceTwoPoints c p' > Utils.squaredDistanceTwoPoints c p
+ else
+ yield sign (c.X - p.X) = sign (c.X - p'.X) && Utils.squaredDistanceTwoPoints c p' < Utils.squaredDistanceTwoPoints c p
+ else
+ yield case1
+ } |> Seq.forall id
+
+ let ellipses = ellipses |> List.map EllipseFlaggedKd
+
+ // 1) Associate touching ellipses with each ellipses and remove ellipse with more than two intersections.
+ let tree = KdTree.Tree.BuildTree ellipses
+ let neighbors (e: EllipseFlaggedKd) : (EllipseFlaggedKd * PointD * PointD) list =
+ if not e.Removed
+ then
+ tree.Search (searchRegion e)
+ // We only keep the ellipses touching 'e'.
+ |> List.choose (fun otherE ->
+ if e <> otherE
+ then
+ match EEOver.EEOverlapArea e otherE with
+ | Some (_, px, _) when px.Length > 2 ->
+ otherE.Removed <- true
+ None
+ | Some (area, px, py) when area > 0.f && px.Length = 2 ->
+ Some (otherE, PointD(px.[0], py.[0]), PointD(px.[1], py.[1]))
+ | _ ->
+ None
+ else
+ None )
+ else
+ []
+
+ // 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
+
+ // 3) Remove ellipses with a high standard deviation (high contrast).
+ let imgData = img.Data
+ let globalStdDeviation = MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(seq {
+ for y in 0 .. h - 1 do
+ for x in 0 .. w - 1 do
+ yield float imgData.[y, x, 0] })
+
+ 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 = 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] })
+
+ 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
+ for e, neighbors in ellipsesWithNeigbors do
+ if not e.Removed
+ then
+ let minX, minY, maxX, maxY = ellipseWindow e
+
+ let mutable area = 0
+ 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
+ let p = PointD(float32 x, float32 y)
+ if pixelOwnedByE p e (neighbors |> List.choose (fun (otherE, p1, p2) -> if otherE.Removed then None else Some (otherE :> Ellipse, Utils.lineFromTwoPoints p1 p2)))
+ then
+ area <- area + 1
+
+ if area < int minArea
+ then
+ e.Removed <- true
+
+ // 5) Define pixels associated to each ellipse and create the cells.
+ ellipsesWithNeigbors
+ |> List.choose (fun (e, neighbors) ->
+ if e.Removed
+ then
+ None
+ else
+ let minX, minY, maxX, maxY = ellipseWindow e
+
+ let infectedPixels = List<Point>()
+ let mutable stainPixels = 0
+ let mutable darkStainPixels = 0
+ let mutable nbElement = 0
+
+ let elements = new Matrix<byte>(maxY - minY + 1, maxX - minX + 1)
+ for y in minY .. maxY do
+ for x in minX .. maxX do
+ let p = PointD(float32 x, float32 y)
+ if pixelOwnedByE p e (neighbors |> List.choose (fun (otherE, p1, p2) -> if otherE.Removed then None else Some (otherE :> Ellipse, Utils.lineFromTwoPoints p1 p2)))
+ then
+ elements.[y-minY, x-minX] <- 1uy
+ nbElement <- nbElement + 1
+
+ if infection.Data.[y, x, 0] > 0uy
+ then
+ infectedPixels.Add(Point(x, y))
+
+ if parasites.stain.Data.[y, x, 0] > 0uy
+ then
+ stainPixels <- stainPixels + 1
+
+ if parasites.darkStain.Data.[y, x, 0] > 0uy
+ then
+ darkStainPixels <- darkStainPixels + 1
+
+ let cellClass =
+ if float darkStainPixels > config.Parameters.maxDarkStainRatio * (float nbElement) ||
+ float stainPixels > config.Parameters.maxStainRatio * (float nbElement)
+ then
+ Peculiar
+ elif infectedPixels.Count >= 1
+ then
+ let infectionToRemove = ImgTools.connectedComponents parasites.stain infectedPixels
+ for p in infectionToRemove do
+ infection.Data.[p.Y, p.X, 0] <- 0uy
+ InfectedRBC
+ else
+ HealthyRBC
+
+ Some { cellClass = cellClass
+ center = Point(roundInt e.Cx, roundInt e.Cy)
+ infectedArea = infectedPixels.Count
+ stainArea = stainPixels
+ elements = elements })