# The Active Segmentation Platform for Microscopic Image Classification and Segmentation

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## Abstract

**:**

## 1. Introduction

- histogram based
- edge-based
- morphological operations
- region-based, such as watersheds
- deformable model fitting

- (1)
- deterministic, in order to ensure reproducibility of the results;
- (2)
- providing an uncertainty measure of the segmentation outcome;
- (3)
- able to provide some insight into how the morphology is objectively described
- (4)
- able to learn new information whenever such is presented

- numerical feature engineering method (NFE);
- Artificial Neural Networks (ANNs);
- Transport based morphometry (TBM);

## 2. Theory

#### 2.1. Geometrical Image Features

#### 2.2. Differential Invariants

#### 2.3. Curvature Invariants

#### 2.4. Structure Tensor

#### 2.5. Scale Space Theory

#### 2.6. Image Moments

#### 2.7. Random Forest Classifier

#### 2.8. Support Vector Machines Classifier

## 3. User Interaction and Architecture

#### 3.1. Architecture

#### 3.2. Operation of the Platform

#### 3.3. Metadata

## 4. Materials and Methods

#### 4.1. Data Sets

#### 4.1.1. Synthetic Data Sets

#### 4.1.2. EM ISBI Challenge Data Set

#### 4.1.3. HeLa Data Set

#### 4.1.4. HEp-2 Data Set

#### 4.2. Image Segmentation Task

#### 4.3. Segmentation Metrics

#### 4.4. Image Classification Task

## 5. Results

#### 5.1. Image Segmentation

#### 5.1.1. Experiment 1: Batch Segmentation and Comparison

#### 5.1.2. Experiment 2: Three Class Segmentation

#### 5.2. Image Classification

#### 5.2.1. Case Study 1: Synthetic Data Set Classification

#### 5.2.2. Case Study 2: HeLa Cells Classification

#### 5.2.3. Case Study 3: HEp2 Cells Classification

#### 5.3. Influence of the Classification Method

## 6. Discussion

#### 6.1. Platform Comparison

#### 6.2. The Problem of Ground Truth

#### 6.3. The Problem of Feature Selection

#### 6.4. Image Classification Aspects

#### 6.5. Outlook

#### 6.6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A. Weak Differentiation

## Appendix B. Orthogonal Laplacian Decomposition

## References

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**Figure 4.**Circles/Triangles classification. (

**A**) An image generated with variable size triangles and circles on black background; (

**B**) Individual objects are classified as triangles (green) and circles (blue) on noiseless background; (

**C**) Individual objects are classified as triangles (green) and circles (blue) on noisy background.

**Figure 5.**A representative image of each protein class and the average number of correctly classified instances in 10 cross validation (

**A**) filamentous actin labeled with rhodamine-phalloidin (Actin); (

**B**) DNA labeled with DAPI (DNA); (

**C**) endoplasmic reticulum protein (ER); (

**D**) transferrin receptor (endosome); (

**E**) Golgi protein giantin (Golgia); (

**F**) Golgi protein GPP130 (Golgpp); (

**G**) lysosomal protein LAMP2 (lysosomes); (

**H**) microtubules; (

**I**) mitochondrial protein (mitochondria); (

**J**) nucleolar protein nucleolin (nucleolus).

**Figure 6.**A representative image of each cell class and the average number of correctly classified instances in 10 cross validation. (

**A**) Centromere (CT); (

**B**) Golgi apparatus (GG); (

**C**) Homogeneous (HM); (

**D**) Nucleolar (NCL); (

**E**) Anc nuclear membrane (ANM); (

**F**) Speckled (SP).

**Figure 7.**Overview of the Image Classification Workflow. (

**A**) A sample image of filamentous actin labeled with rhodamine-phalloidin (Actin) from the HeLa data set; (

**B**) Representative images of ALoG, LoG and BoG at scale 4; (

**C**) Compound image transforms on original and scaled images, Feature vector is computed; (

**D**) CFS based feature selection and SVM’s are used for training the model; (

**E**) Several evaluation metrics are computed e.g., ROC curve, confusion matrix etc.

**Figure 8.**Characteristic features compared to the segmentation outcome. (

**A**) Initial expert annotation. (

**B**) Outcome of the segmentation; (

**C**) Gradient amplitude, $r=8$; (

**D**) Largest Hessian eigenvalue, $r=8$; (

**E**) Normal component ${\Delta}_{\perp}$ of the LoG, $r=8$; (

**F**) Mean curvature, $r=8$. The scale bar represents 0.5 $\mathsf{\mu}$m.

**Figure 9.**Characteristic features compared to the segmentation outcome. (

**A**) RF classifier. (

**B**) SMO classifier.

First Order Invariants | |
---|---|

Gradient amplitude | $A=\sqrt{{G}_{x}^{2}+{G}_{y}^{2}}$ |

Gradient orientation | $sin\varphi ={G}_{y}/\sqrt{{G}_{x}^{2}+{G}_{y}^{2}}$ |

$cos\varphi ={G}_{x}/\sqrt{{G}_{x}^{2}+{G}_{y}^{2}}$ | |

Second order invariants | |

Laplacian | ${\Delta}_{G}=\mathrm{Tr}\phantom{\rule{0.166667em}{0ex}}{\mathbb{H}}_{G}={G}_{xx}+{G}_{yy}$ |

Determinant of the Hessian | $\mathrm{det}\phantom{\rule{0.166667em}{0ex}}{\mathbb{H}}_{G}={G}_{xx}{G}_{yy}-{G}_{xy}^{2}$ |

Curvature Invariant | Formula |
---|---|

Line curvature | $\left(\right)open="("\; close=")">{G}_{xx}{G}_{y}^{2}-2{G}_{x}{G}_{y}{G}_{xy}+{G}_{x}^{2}{G}_{yy}$ |

Mean curvature | $1/2\left(\right)open="("\; close=")">\left(\right)open="("\; close=")">1+{G}_{x}^{2}{G}_{yy}-2{G}_{x}{G}_{y}{G}_{xy}+\left(\right)open="("\; close=")">1+{G}_{y}^{2}$ |

Gaussian curvature | $\left(\right)open="("\; close=")">{G}_{xx}{G}_{yy}-{G}_{xy}^{2}$ |

Filter | Functionality | Feature Order |
---|---|---|

Gauss2D | Gaussian smoothing, Equation (4) | 0 |

Gradient | Gradient amplitude and orientation | 1 |

Gaussian Structure | Structure tensor, Equation (2) | 1 |

LoG | Laplacian of Gaussian (LoG) | 2 |

ALoG | Anisotropic decomposition of LoG, Equations (A2) and (A3) | 2 |

Gradient amplitude and orientation | 1 | |

Hessian | Eigenvalues of the Hessian | 2 |

Determinant of the Hessian | 2 | |

Curvature 2D | Line curvature + Hessian determinant | 2 |

Curvature 3D | Mean + Gauss curvature of surfaces | 2 |

BoG | Bi-Laplacian of Gaussian, Equation (5) | 4 |

Gaussian Jet | Gaussian Jet of order n | n |

LoGN | n-th order PoL, Equation (5) | 2 n |

Regional Feature | Functionality |
---|---|

Legendre | Legendre moments |

Zernike | Zernike moments |

ImageJ | ImageJ statistics |

Haralick | Haralick features [12] |

Data Set | Filters | Min Scale | Max Scale |
---|---|---|---|

HeLa | LoG, ALoG, HoG, Structure | 2 | 8 |

HEp-2 | LoG, BoG, ALoG, HoG, Structure | 2 | 8 |

Regional Feature | Parameters |
---|---|

Legendre | order (0–6) |

Zernike | order (0–6) |

ImageJ | – |

Haralick | distance (1–3), directions (90${}^{\circ}$, 180${}^{\circ}$, 270${}^{\circ}$, 360${}^{\circ}$) |

Metric | Training Set | Test Set | ||
---|---|---|---|---|

AS/IJ | Ilastik | AS/IJ | Ilastik | |

${V}^{rand}$ | 1.0 | 1.0 | 0.87 | 0.91 |

${V}^{info}$ | 1.0 | 1.0 | 0.93 | 0.94 |

TP Rate | FP Rate | Precision | Recall | ${\mathit{F}}_{1}$ | ROC Area | PRC Area | Class |
---|---|---|---|---|---|---|---|

0.954 | 0.081 | 0.942 | 0.954 | 0.948 | 0.943 | 0.932 | cells |

0.722 | 0.020 | 0.808 | 0.722 | 0.763 | 0.935 | 0.667 | boundaries |

0.909 | 0.048 | 0.897 | 0.909 | 0.903 | 0.934 | 0.847 | nuclei |

**Table 9.**Comparison of mean average true positive rate (TP), false positive rate (FP), precision, recall, ${F}_{1}$ score, ROC area and PR area of different classification approaches in different data sets.

Data Set | TP | FP | Precision | Recall | ${\mathit{F}}_{1}$ | ROC Area | PRC Area |
---|---|---|---|---|---|---|---|

Regional Features | |||||||

Hela | 0.82 | 0.02 | 0.82 | 0.81 | 0.81 | 0.96 | 0.75 |

HEp-2 | 0.76 | 0.06 | 0.77 | 0.76 | 0.76 | 0.90 | 0.68 |

Regional features + Scale space pixel-features | |||||||

Hela | 0.92 | 0.01 | 0.92 | 0.92 | 0.92 | 0.98 | 0.88 |

HEp-2 | – | – | – | – | – | – | – |

Regional features + Scale space pixel-features+ discriminative features selection | |||||||

Hela | 0.93 | 0.01 | 0.93 | 0.93 | 0.93 | 0.99 | 0.90 |

HEp-2 | 0.88 | 0.03 | 0.88 | 0.88 | 0.88 | 0.95 | 0.83 |

Classifier | TP | FP | Precision | Recall | ${\mathit{F}}_{1}$ | ROC Area | PRC Area |
---|---|---|---|---|---|---|---|

SMO | 0.806 | 0.021 | 0.805 | 0.806 | 0.804 | 0.955 | 0.729 |

RF | 0.717 | 0.031 | 0.712 | 0.717 | 0.713 | 0.950 | 0.758 |

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## Share and Cite

**MDPI and ACS Style**

Vohra, S.K.; Prodanov, D.
The Active Segmentation Platform for Microscopic Image Classification and Segmentation. *Brain Sci.* **2021**, *11*, 1645.
https://doi.org/10.3390/brainsci11121645

**AMA Style**

Vohra SK, Prodanov D.
The Active Segmentation Platform for Microscopic Image Classification and Segmentation. *Brain Sciences*. 2021; 11(12):1645.
https://doi.org/10.3390/brainsci11121645

**Chicago/Turabian Style**

Vohra, Sumit K., and Dimiter Prodanov.
2021. "The Active Segmentation Platform for Microscopic Image Classification and Segmentation" *Brain Sciences* 11, no. 12: 1645.
https://doi.org/10.3390/brainsci11121645