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Image segmentation

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Image segmentation has many applications in biomedical and many other fields. It is essential for target detection and identification. The project will review existing algorithms and develop new and improved ones. As often used fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbours and clustering centres. Aims are to improve accuracy and to reduce complexity.

This project will involve programming, signal processing, machine learning, mathematical analysis, and good writing ability for presentation of technical work. An ideal candidate will have a very good Master degree or a First Class Bachelor degree.

Funding Notes

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: View Website. Recently the UK Government made available the Doctoral Student Loans of up to £25,000 for UK and EU students and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.)

References

Below are some publications from my group. These will give you good indications of the work we have done already and the developments of our ideas, techniques, and implementations.
1. T Lei, P Liu, X Jia, X Zhang, H Meng, and A K Nandi, "Automatic fuzzy clustering framework for image segmentation", IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2019.2930030, accepted, 2019.

2. T Lei, X Jia, T Liu, S Liu, H Meng, and A K Nandi, "Adaptive morphological reconstruction for seeded image segmentation", IEEE Transactions on Image Processing, DOI: 10.1109/TIP.2019.2920514, vol. 28, no. 11, pp. 5510-5523, 2019.

3. T Lei, X Jia, Y Zhang, S Liu, H Meng, and A K Nandi, "Superpixel-based fast fuzzy C-means clustering for color image segmentation", IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2018.2889018, vol. 27, no. 9, pp. 1753-1766, 2019.

4. T Lei, Y Zhang, Z Lv, S Liu, S Liu, and A K Nandi, "Landslide inventory mapping from bi-temporal images using deep convolution networks", IEEE Geoscience and Remote Sensing Letters, DOI: 10.1109/LGRS.2018.2889307, vol. 16, no. 6, pp. 982-986, 2019.

5. T Lei, X Jia, Y Zhang, L He, H Meng, and A K Nandi, "Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering", IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2018.2796074, vol. 26, no. 5, pp. 3027-3041, 2018.

6. T Lei, D Xue, Z Lv, S Li, Y Zhang, and A K Nandi, "Unsupervised change detection using fast fuzzy clustering for landslide mapping from very high-resolution images", Remote Sensing, DOI: 10.3390/RS10091381, vol. 10, no. 9, 1381 (23 pages), 2018.


How good is research at Brunel University London in General Engineering?

FTE Category A staff submitted: 63.45

Research output data provided by the Research Excellence Framework (REF)

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