# An Improved OTSU Algorithm Using Histogram Accumulation Moment for Ore Segmentation

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## Abstract

**:**

## 1. Introduction

- (1)
- The object contrast in dark areas is improved and the shadow from stacked and adhesive ores is eliminated by using the multi-scale Retinex color restoration algorithm;
- (2)
- An improved OTSU algorithm is proposed. It reduces the error caused by image background and noise by calculating the gray-level zero-order and first-order cumulative moments closed to the selected area. The selected gray level gradually approaches the optimal threshold to avoid falling into the local optimum. This algorithm can be used in the segmentation of mineral images with unimodal or insignificant bimodal properties;
- (3)
- The validity and accuracy of the proposed algorithm is verified from the qualitative and quantitative analysis in multi-scenario ore image segmentation.

## 2. Problems of OTSU for Ore Segmentation

- (1)
- The ore image is affected by uneven illumination, dust and mud coverage. The noise is serious, the target edge is blurred and the contrast between the object and background is low;
- (2)
- The mineral is scattered, bulky and stacked. Due to the crushing, the ore particles with sharp edges, angular corners and complex internal texture are stacked and adhered;
- (3)
- The object and the background pixels in the image are cross-aliased, mostly resulting in unimodal or inconspicuous bimodal gray histogram distribution.

## 3. Improved Ore Image Segmentation Algorithm

#### 3.1. Multiscale Retinex Color Restoration Algorithm

#### 3.2. Improved OTSU Segmentation Based on Histogram Accumulation Moment

#### 3.2.1. The Selection of Random Gray Value

#### 3.2.2. Improvement of Zero-Order and First-Order Cumulative Moments

#### 3.2.3. Selection of Optimal Threshold

#### 3.2.4. Image Binarization

## 4. Experimental Results and Analysis

#### 4.1. Qualitative Analysis

#### 4.2. Quantitative Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 6.**The comparison of ore segmentation result. (

**a**) is coarse ore; (

**b**) is middle ore; (

**c**) is fine ore; (

**d**–

**f**) are OTSU results for (

**a**–

**c**); (

**g**–

**i**) are double-window OTSU results for (

**a**–

**c**); (

**j**–

**l**) are the HCM_OTSU results for (

**a**–

**c**).

Data Set | OTSU | Double-Window OTSU | Our Method | |||
---|---|---|---|---|---|---|

Fnr | Fpr | Fnr | Fpr | Fnr | Fpr | |

Coarse images | 0.259 | 0.057 | 0.386 | 0.063 | 0.015 | 0.008 |

Middle image | 0.309 | 0.284 | 0.312 | 0.127 | 0.006 | 0.139 |

Fine image | 0.587 | 0.343 | 0.363 | 0.455 | 0.285 | 0.298 |

Detection Indicator | OTSU | Double-Window OTSU | Our Method |
---|---|---|---|

DR | 55.1% | 60.3% | 89.6% |

FAR | 36.9% | 63.2% | 17.4% |

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**MDPI and ACS Style**

Zhan, Y.; Zhang, G.
An Improved OTSU Algorithm Using Histogram Accumulation Moment for Ore Segmentation. *Symmetry* **2019**, *11*, 431.
https://doi.org/10.3390/sym11030431

**AMA Style**

Zhan Y, Zhang G.
An Improved OTSU Algorithm Using Histogram Accumulation Moment for Ore Segmentation. *Symmetry*. 2019; 11(3):431.
https://doi.org/10.3390/sym11030431

**Chicago/Turabian Style**

Zhan, Yantong, and Guoying Zhang.
2019. "An Improved OTSU Algorithm Using Histogram Accumulation Moment for Ore Segmentation" *Symmetry* 11, no. 3: 431.
https://doi.org/10.3390/sym11030431