Automated segmentation and classification of unlabelled malaria parasites in red blood cells from phase contrast images
An automated method for detection of unlabelled malaria parasites in red blood cells is presented. From phase-constrast microscopy images of live red blood cells, the proposed algorithm segments red blood cells and classifies them into uninfected and infected cells. This approach can be used to o...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/10356/68682 |
| _version_ | 1826123852222038016 |
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| author | Mathew Athul Mangalathumannil |
| author2 | Justin Dauwels |
| author_facet | Justin Dauwels Mathew Athul Mangalathumannil |
| author_sort | Mathew Athul Mangalathumannil |
| collection | NTU |
| description | An automated method for detection of unlabelled malaria parasites in red blood
cells is presented. From phase-constrast microscopy images of live red blood cells,
the proposed algorithm segments red blood cells and classifies them into uninfected
and infected cells. This approach can be used to observe the progression of malaria
parasites through different stages as the algorithm presented can be used on unstained
images. In related work, classification is performed on stained images. The image
processing framework consists of image pre-processing, segmentation and classification
of infected and uninfected cells from phase contrast images. Two segmentation
methods; morphological operations and template matching, are applied and compared.
To separate connected cells, a watershed algorithm is applied. The final goal of this
project is to detect malaria infected cells from microscopic blood images at lowest
magnification and then control the imaging system in the microscope to automatically
zoom to monitor the infected cells. Manual detection of malaria from a cell sample
is a very tedious and time consuming process. Using our methodology we are able
to differentiate infected and non-infected cells automatically. Our approach also
bypasses the need to stain the cell samples. This work can thus be implemented for
fast, simple to execute and efficient diagnosis of malaria infected cells. |
| first_indexed | 2024-10-01T06:11:06Z |
| format | Thesis |
| id | ntu-10356/68682 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:11:06Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/686822023-07-04T15:04:53Z Automated segmentation and classification of unlabelled malaria parasites in red blood cells from phase contrast images Mathew Athul Mangalathumannil Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering An automated method for detection of unlabelled malaria parasites in red blood cells is presented. From phase-constrast microscopy images of live red blood cells, the proposed algorithm segments red blood cells and classifies them into uninfected and infected cells. This approach can be used to observe the progression of malaria parasites through different stages as the algorithm presented can be used on unstained images. In related work, classification is performed on stained images. The image processing framework consists of image pre-processing, segmentation and classification of infected and uninfected cells from phase contrast images. Two segmentation methods; morphological operations and template matching, are applied and compared. To separate connected cells, a watershed algorithm is applied. The final goal of this project is to detect malaria infected cells from microscopic blood images at lowest magnification and then control the imaging system in the microscope to automatically zoom to monitor the infected cells. Manual detection of malaria from a cell sample is a very tedious and time consuming process. Using our methodology we are able to differentiate infected and non-infected cells automatically. Our approach also bypasses the need to stain the cell samples. This work can thus be implemented for fast, simple to execute and efficient diagnosis of malaria infected cells. Master of Science (Computer Control and Automation) 2016-05-30T08:52:30Z 2016-05-30T08:52:30Z 2016 Thesis http://hdl.handle.net/10356/68682 en 63 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Mathew Athul Mangalathumannil Automated segmentation and classification of unlabelled malaria parasites in red blood cells from phase contrast images |
| title | Automated segmentation and classification of unlabelled malaria parasites in red blood cells from phase contrast images |
| title_full | Automated segmentation and classification of unlabelled malaria parasites in red blood cells from phase contrast images |
| title_fullStr | Automated segmentation and classification of unlabelled malaria parasites in red blood cells from phase contrast images |
| title_full_unstemmed | Automated segmentation and classification of unlabelled malaria parasites in red blood cells from phase contrast images |
| title_short | Automated segmentation and classification of unlabelled malaria parasites in red blood cells from phase contrast images |
| title_sort | automated segmentation and classification of unlabelled malaria parasites in red blood cells from phase contrast images |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/68682 |
| work_keys_str_mv | AT mathewathulmangalathumannil automatedsegmentationandclassificationofunlabelledmalariaparasitesinredbloodcellsfromphasecontrastimages |