* Add area granulometry (not used for the moment)
[master-thesis.git] / Parasitemia / Parasitemia / Classifier.fs
1 module Classifier
2
3 open System
4 open System.Collections.Generic
5 open System.Drawing
6
7 open Emgu.CV
8 open Emgu.CV.Structure
9
10 open Types
11 open Utils
12
13
14 type private EllipseFlaggedKd (e: Ellipse) =
15 inherit Ellipse (e.Cx, e.Cy, e.A, e.B, e.Alpha)
16
17 member val Removed = false with get, set
18
19 interface KdTree.I2DCoords with
20 member this.X = this.Cx
21 member this.Y = this.Cy
22
23
24 let findCells (ellipses: Ellipse list) (parasites: ParasitesMarker.Result) (img: Image<Gray, float32>) (config: Config.Config) : Cell list =
25 if ellipses.IsEmpty
26 then
27 []
28 else
29 let infection = parasites.infection.Copy() // To avoid to modify the parameter.
30
31 // This is the minimum window size to check if other ellipses touch 'e'.
32 let searchRegion (e: Ellipse) = { KdTree.minX = e.Cx - (e.A + config.RBCMaxRadius)
33 KdTree.maxX = e.Cx + (e.A + config.RBCMaxRadius)
34 KdTree.minY = e.Cy - (e.A + config.RBCMaxRadius)
35 KdTree.maxY = e.Cy + (e.A + config.RBCMaxRadius) }
36
37 // The minimum window to contain a given ellipse.
38 let ellipseWindow (e: Ellipse) =
39 let cx, cy = roundInt e.Cx, roundInt e.Cy
40 let a = int (e.A + 0.5f)
41 cx - a, cy - a, cx + a, cy + a
42
43 let w = img.Width
44 let w_f = float32 w
45 let h = img.Height
46 let h_f = float32 h
47
48 // Return 'true' if the point 'p' is owned by e.
49 // The lines represents all intersections with other ellipses.
50 let pixelOwnedByE (p: PointD) (e: Ellipse) (others: (Ellipse * Line) list) =
51 e.Contains p.X p.Y &&
52 seq {
53 let c = PointD(e.Cx, e.Cy)
54 for e', d1 in others do
55 let d2 = Utils.lineFromTwoPoints c p
56 let c' = PointD(e'.Cx, e'.Cy)
57 let v = pointFromTwoLines d1 (lineFromTwoPoints c c')
58 let case1 = sign (v.X - c.X) <> sign (v.X - c'.X) || Utils.squaredDistanceTwoPoints v c > Utils.squaredDistanceTwoPoints v c'
59 if d2.Valid
60 then
61 let p' = Utils.pointFromTwoLines d1 d2
62 // Yield 'false' when the point is owned by another ellipse.
63 if case1
64 then
65 yield sign (c.X - p.X) <> sign (c.X - p'.X) || Utils.squaredDistanceTwoPoints c p' > Utils.squaredDistanceTwoPoints c p
66 else
67 yield sign (c.X - p.X) = sign (c.X - p'.X) && Utils.squaredDistanceTwoPoints c p' < Utils.squaredDistanceTwoPoints c p
68 else
69 yield case1
70 } |> Seq.forall id
71
72 let ellipses = ellipses |> List.map EllipseFlaggedKd
73
74 // 1) Associate touching ellipses with each ellipses and remove ellipse with more than two intersections.
75 let tree = KdTree.Tree.BuildTree ellipses
76 let neighbors (e: EllipseFlaggedKd) : (EllipseFlaggedKd * PointD * PointD) list =
77 if not e.Removed
78 then
79 tree.Search (searchRegion e)
80 // We only keep the ellipses touching 'e'.
81 |> List.choose (fun otherE ->
82 if e <> otherE
83 then
84 match EEOver.EEOverlapArea e otherE with
85 | Some (_, px, _) when px.Length > 2 ->
86 otherE.Removed <- true
87 None
88 | Some (area, px, py) when area > 0.f && px.Length = 2 ->
89 Some (otherE, PointD(px.[0], py.[0]), PointD(px.[1], py.[1]))
90 | _ ->
91 None
92 else
93 None )
94 else
95 []
96
97 // We reverse the list to get the lower score ellipses first.
98 let ellipsesWithNeigbors = ellipses |> List.map (fun e -> e, neighbors e) |> List.rev
99
100
101 // 2) Remove ellipses touching the edges.
102 for e in ellipses do
103 if e.isOutside w_f h_f then e.Removed <- true
104
105 // 3) Remove ellipses with a high standard deviation (high contrast).
106 let imgData = img.Data
107 let globalStdDeviation = MathNet.Numerics.Statistics.Statistics.PopulationStandardDeviation(seq {
108 for y in 0 .. h - 1 do
109 for x in 0 .. w - 1 do
110 yield float imgData.[y, x, 0] })
111
112 for e in ellipses do
113 if not e.Removed
114 then
115 let shrinkedE = e.Scale 0.9f
116 let minX, minY, maxX, maxY = ellipseWindow shrinkedE
117
118 let stdDeviation = MathNet.Numerics.Statistics.Statistics.StandardDeviation (seq {
119 for y in (if minY < 0 then 0 else minY) .. (if maxY >= h then h - 1 else maxY) do
120 for x in (if minX < 0 then 0 else minX) .. (if maxX >= w then w - 1 else maxX) do
121 if shrinkedE.Contains (float32 x) (float32 y)
122 then
123 yield float imgData.[y, x, 0] })
124
125 if stdDeviation > globalStdDeviation * config.Parameters.standardDeviationMaxRatio then
126 e.Removed <- true
127
128 (*
129 let imgData = img.Data
130 let stdDeviations = [
131 for e in ellipses do
132 if not e.Removed
133 then
134 let shrinkedE = e.Scale 0.9f
135 let minX, minY, maxX, maxY = ellipseWindow shrinkedE
136
137 let stdDeviation = float32 <| MathNet.Numerics.Statistics.Statistics.StandardDeviation (seq {
138 for y in (if minY < 0 then 0 else minY) .. (if maxY >= h then h - 1 else maxY) do
139 for x in (if minX < 0 then 0 else minX) .. (if maxX >= w then w - 1 else maxX) do
140 if shrinkedE.Contains (float32 x) (float32 y)
141 then
142 yield float imgData.[y, x, 0] })
143
144 e.StdDeviation <- stdDeviation
145 yield stdDeviation ]
146
147 // We use Otsu and eliminate some cells only if the curve may be bimodal.
148 // See https://en.wikipedia.org/wiki/Multimodal_distribution#Bimodality_coefficient
149 let skewness, kurtosis = MathNet.Numerics.Statistics.Statistics.PopulationSkewnessKurtosis (stdDeviations |> List.map float)
150 let n = float stdDeviations.Length
151 let bimodalityCoefficient = (skewness ** 2. + 1.) / (kurtosis + 3. * (n - 1.) ** 2. / ((n - 2.) * (n - 3.)))
152
153 if bimodalityCoefficient > 5. / 9.
154 then
155 let hist = ImgTools.histogram stdDeviations 200
156 let thresh, _, _ = ImgTools.otsu hist
157 for e in ellipses do
158 if not e.Removed && e.StdDeviation > thresh
159 then e.Removed <- true
160 *)
161
162 // 4) Remove ellipses with little area.
163 let minArea = config.RBCMinArea
164 for e, neighbors in ellipsesWithNeigbors do
165 if not e.Removed
166 then
167 let minX, minY, maxX, maxY = ellipseWindow e
168
169 let mutable area = 0
170 for y in (if minY < 0 then 0 else minY) .. (if maxY >= h then h - 1 else maxY) do
171 for x in (if minX < 0 then 0 else minX) .. (if maxX >= w then w - 1 else maxX) do
172 let p = PointD(float32 x, float32 y)
173 if pixelOwnedByE p e (neighbors |> List.choose (fun (otherE, p1, p2) -> if otherE.Removed then None else Some (otherE :> Ellipse, Utils.lineFromTwoPoints p1 p2)))
174 then
175 area <- area + 1
176
177 if area < int minArea
178 then
179 e.Removed <- true
180
181 // 5) Define pixels associated to each ellipse and create the cells.
182 ellipsesWithNeigbors
183 |> List.choose (fun (e, neighbors) ->
184 if e.Removed
185 then
186 None
187 else
188 let minX, minY, maxX, maxY = ellipseWindow e
189
190 let infectedPixels = List<Point>()
191 let mutable stainPixels = 0
192 let mutable darkStainPixels = 0
193 let mutable nbElement = 0
194
195 let elements = new Matrix<byte>(maxY - minY + 1, maxX - minX + 1)
196 for y in minY .. maxY do
197 for x in minX .. maxX do
198 let p = PointD(float32 x, float32 y)
199 if pixelOwnedByE p e (neighbors |> List.choose (fun (otherE, p1, p2) -> if otherE.Removed then None else Some (otherE :> Ellipse, Utils.lineFromTwoPoints p1 p2)))
200 then
201 elements.[y-minY, x-minX] <- 1uy
202 nbElement <- nbElement + 1
203
204 if infection.Data.[y, x, 0] > 0uy
205 then
206 infectedPixels.Add(Point(x, y))
207
208 if parasites.stain.Data.[y, x, 0] > 0uy
209 then
210 stainPixels <- stainPixels + 1
211
212 if parasites.darkStain.Data.[y, x, 0] > 0uy
213 then
214 darkStainPixels <- darkStainPixels + 1
215
216 let cellClass =
217 if float darkStainPixels > config.Parameters.maxDarkStainRatio * (float nbElement) ||
218 float stainPixels > config.Parameters.maxStainRatio * (float nbElement)
219 then
220 Peculiar
221 elif infectedPixels.Count >= 1
222 then
223 let infectionToRemove = ImgTools.connectedComponents parasites.stain infectedPixels
224 for p in infectionToRemove do
225 infection.Data.[p.Y, p.X, 0] <- 0uy
226 InfectedRBC
227 else
228 HealthyRBC
229
230 Some { cellClass = cellClass
231 center = Point(roundInt e.Cx, roundInt e.Cy)
232 stainArea = stainPixels
233 elements = elements })