We are asked to implement a univariate Gaussian classifier, and use it to classify 6 different images, using features obtained from these images. I will only list the results in the following, but the source for obtaining these results is available here.

The following masks where used to obtain training features, and test features, respectively.

Figure 0.1 Train mask (left), and test mask (right).

Feature image 1

Figure 1.1 Original `tm1.png`.
Figure 1.2 Feature value distribution from training, `tm1.png`.
Figure 1.3 Posterior images from `tm1.png`.
Figure 1.4 Classification result on `tm1.png`.
Measure Class 1 Class 2 Class 3 Class 4
RP 1,729 2,824 1,979 1,605
RN 6,408 5,313 6,158 6,532
PP 1,591 1,827 2,346 2,373
PN 6,546 6,310 5,791 5,764
TP 416 1,816 1,647 1,118
FP 1,175 11 699 1,255
FN 1,313 1,008 332 487
TN 5,233 5,302 5,459 5,277
         
tpr 0.24 0.64 0.83 0.70
tnr 0.82 1.00 0.89 0.81
ppv 0.26 0.99 0.70 0.47
npv 0.80 0.84 0.94 0.92
acc 0.69 0.87 0.87 0.79
iou 0.14 0.64 0.62 0.39
dsc 0.25 0.78 0.76 0.56
auc 0.53 0.82 0.86 0.75

Feature image 2

Figure 2.1 Original `tm2.png`.
Figure 2.2 Feature value distribution from training, `tm2.png`.
Figure 2.3 Posterior images from `tm2.png`.
Figure 2.4 Classification result on `tm2.png`.
Measure Class 1 Class 2 Class 3 Class 4
RP 1,729 2,824 1,979 1,605
RN 6,408 5,313 6,158 6,532
PP 926 3,060 2,187 1,964
PN 7,211 5,077 5,950 6,173
TP 452 2,700 1,813 972
FP 474 360 374 992
FN 1,277 124 166 633
TN 5,934 4,953 5,784 5,540
         
tpr 0.26 0.96 0.92 0.61
tnr 0.93 0.93 0.94 0.85
ppv 0.49 0.88 0.83 0.49
npv 0.82 0.98 0.97 0.90
acc 0.78 0.94 0.93 0.80
iou 0.21 0.85 0.77 0.37
dsc 0.34 0.92 0.87 0.54
auc 0.59 0.94 0.93 0.73

Feature image 3

Figure 3.1 Original `tm1.png`.
Figure 3.2 Feature value distribution from training, `tm3.png`.
Figure 3.3 Posterior images from `tm3.png`.
Figure 3.4 Classification result on `tm3.png`.
Measure Class 1 Class 2 Class 3 Class 4
RP 1,729 2,824 1,979 1,605
RN 6,408 5,313 6,158 6,532
PP 1,653 2,297 2,267 1,920
PN 6,484 5,840 5,870 6,217
TP 708 2,286 1,825 1,033
FP 945 11 442 887
FN 1,021 538 154 572
TN 5,463 5,302 5,716 5,645
         
tpr 0.41 0.81 0.92 0.64
tnr 0.85 1.00 0.93 0.86
ppv 0.43 1.00 0.81 0.54
npv 0.84 0.91 0.97 0.91
acc 0.76 0.93 0.93 0.82
iou 0.26 0.81 0.75 0.41
dsc 0.42 0.89 0.86 0.59
auc 0.63 0.90 0.93 0.75

Feature image 4

Figure 4.1 Original `tm4.png`.
Figure 4.2 Feature value distribution from training, `tm4.png`.
Figure 4.3 Posterior images from `tm4.png`.
Figure 4.4 Classification result on `tm4.png`.
Measure Class 1 Class 2 Class 3 Class 4
RP 1,729 2,824 1,979 1,605
RN 6,408 5,313 6,158 6,532
PP 2,921 0 2,132 3,084
PN 5,216 8,137 6,005 5,053
TP 1,417 0 1,594 1,258
FP 1,504 0 538 1,826
FN 312 2,824 385 347
TN 4,904 5,313 5,620 4,706
         
tpr 0.82 0.00 0.81 0.78
tnr 0.77 1.00 0.91 0.72
ppv 0.49 nan 0.75 0.41
npv 0.94 0.65 0.94 0.93
acc 0.78 0.65 0.89 0.73
iou 0.44 0.00 0.63 0.37
dsc 0.61 0.00 0.78 0.54
auc 0.79 0.50 0.86 0.75

Feature image 5

Figure 5.1 Original `tm5.png`.
Figure 5.2 Feature value distribution from training, `tm5.png`.
Figure 5.3 Posterior images from `tm5.png`.
Figure 5.4 Classification result on `tm5.png`.
Measure Class 1 Class 2 Class 3 Class 4
RP 1,729 2,824 1,979 1,605
RN 6,408 5,313 6,158 6,532
PP 1,330 1,996 2,072 2,739
PN 6,807 6,141 6,065 5,398
TP 814 1,875 1,913 1,133
FP 516 121 159 1,606
FN 915 949 66 472
TN 5,892 5,192 5,999 4,926
         
tpr 0.47 0.66 0.97 0.71
tnr 0.92 0.98 0.97 0.75
ppv 0.61 0.94 0.92 0.41
npv 0.87 0.85 0.99 0.91
acc 0.82 0.87 0.97 0.74
iou 0.36 0.64 0.89 0.35
dsc 0.53 0.78 0.94 0.52
auc 0.70 0.82 0.97 0.73

Feature image 6

Figure 6.1 Original `tm1.png`.
Figure 6.2 Feature value distribution from training, `tm6.png`.
Figure 6.3 Posterior images from `tm6.png`.
Figure 6.4 Classification result on `tm6.png`.
Measure Class 1 Class 2 Class 3 Class 4
RP 1,729 2,824 1,979 1,605
RN 6,408 5,313 6,158 6,532
PP 938 1,913 2,481 2,805
PN 7,199 6,224 5,656 5,332
TP 442 1,833 1,915 1,307
FP 496 80 566 1,498
FN 1,287 991 64 298
TN 5,912 5,233 5,592 5,034
         
tpr 0.26 0.65 0.97 0.81
tnr 0.92 0.98 0.91 0.77
ppv 0.47 0.96 0.77 0.47
npv 0.82 0.84 0.99 0.94
acc 0.78 0.87 0.92 0.78
iou 0.20 0.63 0.75 0.42
dsc 0.33 0.77 0.86 0.59
auc 0.59 0.82 0.94 0.79