# Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Problem Formulation + MRF Model

#### 2.2. Initialization–Probability of the Label Random Field

#### 2.3. Pixel Feature Extraction

#### 2.4. Regional Feature Extraction

#### 2.5. Parameters Setting

## 3. Results

#### 3.1. Datasets

#### 3.2. Evaluation Metric Calculation

#### 3.3. Performance Evaluation

#### 3.4. Comparison

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

MRF | Markove Random Filed |

ISIC | International Skin Imaging Collaboration |

MAP | Maximum a Posteriori |

## References

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**Figure 1.**The pixel-pased MRF model part, with observations (squares) and labels (circles) [11].

**Figure 3.**PH2 sample of skin cancer segmentation using the proposed method. (I) Original image (II) Segmented image (III) Segmented lesion region overlapped with the original image

Metric | PH2 Dataset | ISIC Validation | ISIC Test |
---|---|---|---|

Jaccard Index (%) | $78.35$ | $79.78$ | $72.45$ |

Dice Coefficient (%) | $89.65$ | $88.34$ | $80.67$ |

Accuracy (%) | $91.51$ | $92.77$ | $89.47$ |

Sensitivity (%) | $84.07$ | $87.17$ | $79.45$ |

Specificity (%) | $95.55$ | $97.99$ | $95.09$ |

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**MDPI and ACS Style**

Salih, O.; Viriri, S.
Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field. *Symmetry* **2020**, *12*, 1224.
https://doi.org/10.3390/sym12081224

**AMA Style**

Salih O, Viriri S.
Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field. *Symmetry*. 2020; 12(8):1224.
https://doi.org/10.3390/sym12081224

**Chicago/Turabian Style**

Salih, Omran, and Serestina Viriri.
2020. "Skin Lesion Segmentation Using Stochastic Region-Merging and Pixel-Based Markov Random Field" *Symmetry* 12, no. 8: 1224.
https://doi.org/10.3390/sym12081224