- Project name
- Boundary detection and segmentation of live cells in bright-field light microscopy images
- MSc. Ali Ghaznavi
- Description of the activities of the project
- Image Object Detection can be defined as a procedure to localize a region of interest (ROI) in an image by utilizing artificial inteligence methods. Detection and Segmentation Process can be involved in separating an image foreground from its background, by classification or clustering colour or shape similarities between pixel regions.
- The main goal of the project focus on cell boundary detection and segmentation in bright-field light microscopy images based on image processing and machine learning techniques. In order to accomplish this aim, supervised learning-based method will be applied to extract features from region of images and classify them into the background and foreground group by classification method to determine the border of the cells and segment image region which contain our cells or organs.
- The students will work with bright-field light microscope (Superscope) to collect data from MG63 living human cells and develop supervised learning base algorithm for detection and segmentation phase.
- We expect that the proposed system should be invariant to cell size, orientation and illumination conditions and have efficient performance in poorly defined cell boundary and noisy images with inhomogeneous background which are very common problem in microscopic image processing system.
- Number of students - 1-
- high school students students - NO
- university students - YES
- Prepositions (what should the students know): Candidates should be familiar working with Matlab programming language. Candidate should be experienced working with Image processing and machine learning methods.
Academic and University Center
Zamek 136, Nove Hrady