Image Segmentation using Enhanced K-means Clustering with Hybrid Image Filtering for Acute Leukemia Blood Cells Microscopic Images
In biomedical application, image processing become an interesting area that is considered as important role to perform further diagnosis or other task. Segmenting images is consider one of the important steps in image processing stage in digital image processing due to its wide spread usage and appl...
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| id |
my-usim-ddms-12536 |
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uketd_dc |
| institution |
Universiti Sains Islam Malaysia |
| collection |
USIM Institutional Repository |
| language |
English |
| topic |
Leukaemia Microscopic image data Computer applications -- Clinical pathology Medical technology |
| spellingShingle |
Leukaemia Microscopic image data Computer applications -- Clinical pathology Medical technology Farah Hanim Bt Abd Jabar Image Segmentation using Enhanced K-means Clustering with Hybrid Image Filtering for Acute Leukemia Blood Cells Microscopic Images |
| description |
In biomedical application, image processing become an interesting area that is considered as important role to perform further diagnosis or other task. Segmenting images is consider one of the important steps in image processing stage in digital image processing due to its wide spread usage and applications. Recently many researchers have performed many research in assisting the haematologists to segment the leukocytes region from microscopic image of the blood cells in the issue of detecting the leukaemia cells in the early of prognosis. During the post processing, image filtering can cause some discrepancies on the processed image which may lead to insignificant result. The main objective of this research is to develop a method that capable to detect and segment the blood cell automatically for 100 microscopic images of patients suffering from acute leukaemia. The data was collected from the Department of Haematology, Universiti Sains Islam Malaysia, in Malaysia. Three clustering methods heve been utilised to perform the segmentation, which are Standard K-Means (SKM),Fuzzy C-Means and an enhanced method of K-Means algorithm (EKM) that facilitate the mean value of the k-centroids during initialization stage. Finally, image filtering system as background subtraction have been utilized namely Automated Thresholding (AT), Seeded Region Growing (SRG) and Mean Shift (MS) algorithm and the performances are analysed and compared to remove the background scene. The integrated clustering techniques using the Enhanced K-Means clustering together with hybrid image filtering system (MS-AT) as background subtraction have produced tremendous output images without the background scene. Experimental results shows a promising results performance of segmenting the blast cell of 100 microscopic image data using the EKM clustering and hybrid filtering system with the highest score of 99%. Briefly, this research present the advanced computational techniques for processing of microscopic images of blood samples from patients suffering from leukaemia. |
| format |
Thesis |
| author |
Farah Hanim Bt Abd Jabar |
| author_facet |
Farah Hanim Bt Abd Jabar |
| author_sort |
Farah Hanim Bt Abd Jabar |
| title |
Image Segmentation using Enhanced K-means Clustering with Hybrid Image Filtering for Acute Leukemia Blood Cells Microscopic Images |
| title_short |
Image Segmentation using Enhanced K-means Clustering with Hybrid Image Filtering for Acute Leukemia Blood Cells Microscopic Images |
| title_full |
Image Segmentation using Enhanced K-means Clustering with Hybrid Image Filtering for Acute Leukemia Blood Cells Microscopic Images |
| title_fullStr |
Image Segmentation using Enhanced K-means Clustering with Hybrid Image Filtering for Acute Leukemia Blood Cells Microscopic Images |
| title_full_unstemmed |
Image Segmentation using Enhanced K-means Clustering with Hybrid Image Filtering for Acute Leukemia Blood Cells Microscopic Images |
| title_sort |
image segmentation using enhanced k-means clustering with hybrid image filtering for acute leukemia blood cells microscopic images |
| granting_institution |
Universiti Sains Islam Malaysia |
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1812444795494203392 |
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my-usim-ddms-125362024-05-29T19:41:22Z Image Segmentation using Enhanced K-means Clustering with Hybrid Image Filtering for Acute Leukemia Blood Cells Microscopic Images Farah Hanim Bt Abd Jabar In biomedical application, image processing become an interesting area that is considered as important role to perform further diagnosis or other task. Segmenting images is consider one of the important steps in image processing stage in digital image processing due to its wide spread usage and applications. Recently many researchers have performed many research in assisting the haematologists to segment the leukocytes region from microscopic image of the blood cells in the issue of detecting the leukaemia cells in the early of prognosis. During the post processing, image filtering can cause some discrepancies on the processed image which may lead to insignificant result. The main objective of this research is to develop a method that capable to detect and segment the blood cell automatically for 100 microscopic images of patients suffering from acute leukaemia. The data was collected from the Department of Haematology, Universiti Sains Islam Malaysia, in Malaysia. Three clustering methods heve been utilised to perform the segmentation, which are Standard K-Means (SKM),Fuzzy C-Means and an enhanced method of K-Means algorithm (EKM) that facilitate the mean value of the k-centroids during initialization stage. Finally, image filtering system as background subtraction have been utilized namely Automated Thresholding (AT), Seeded Region Growing (SRG) and Mean Shift (MS) algorithm and the performances are analysed and compared to remove the background scene. The integrated clustering techniques using the Enhanced K-Means clustering together with hybrid image filtering system (MS-AT) as background subtraction have produced tremendous output images without the background scene. Experimental results shows a promising results performance of segmenting the blast cell of 100 microscopic image data using the EKM clustering and hybrid filtering system with the highest score of 99%. Briefly, this research present the advanced computational techniques for processing of microscopic images of blood samples from patients suffering from leukaemia. Universiti Sains Islam Malaysia 2016 Thesis en https://oarep.usim.edu.my/handle/123456789/12536 https://oarep.usim.edu.my/bitstreams/9e84a81f-a1e1-4b10-839c-30f7c0201116/download 8a4605be74aa9ea9d79846c1fba20a33 https://oarep.usim.edu.my/bitstreams/bd4080b7-03a7-4b73-a756-31dc38fd073a/download b00345b1c81629e6f6d7a77160b5d895 https://oarep.usim.edu.my/bitstreams/9f96494b-1b12-4202-b6c2-35b56c20f3b0/download 57ea71dc1f2f0e2d5bc0c547d5d833f5 https://oarep.usim.edu.my/bitstreams/44bd8213-8eff-4f80-9322-fc657b59135d/download 6b8f57ec7340cf0c3bfcb32ef63e924e https://oarep.usim.edu.my/bitstreams/b00ab33a-abaf-4b82-a93e-8f63711b3407/download b4d333c1df563a553bd5865dbf0d9660 https://oarep.usim.edu.my/bitstreams/23ea3b80-2270-4941-adbc-388a3fa20146/download 3166ec12eb6b1ca23a55c9369aea74ac https://oarep.usim.edu.my/bitstreams/a3a635e2-5c4e-45ff-ad56-63e998cc75eb/download c2825bcac2636b71376fc516266a5e0c https://oarep.usim.edu.my/bitstreams/617be4af-6d71-411b-964c-7480f782e056/download 30bfa174ecefdaf77bf2b157984d53fe https://oarep.usim.edu.my/bitstreams/84b28033-1f12-4b48-91db-396ef9103b0c/download 0917d3e946e8a70066e77edc2fadc12e https://oarep.usim.edu.my/bitstreams/0da4b447-0818-4746-b392-20a61986bfa4/download f229d9612df79cdd0a4bf559f9c920dd https://oarep.usim.edu.my/bitstreams/a1fae9c7-f81a-4049-a199-d6d2032d14a4/download 68b329da9893e34099c7d8ad5cb9c940 https://oarep.usim.edu.my/bitstreams/39dac9cf-eea4-4d18-8a6c-7d908eee385a/download 405ee2d7512240932976e0fcd4efc518 https://oarep.usim.edu.my/bitstreams/5fe0698b-74f9-4a24-b293-c98fdc6f99b0/download b41aec65c5687c7b1248864a87417848 https://oarep.usim.edu.my/bitstreams/6b49e319-8724-4a2b-bdf3-efab726fe3d1/download 4240a866752bd85877550c46e0e48c85 https://oarep.usim.edu.my/bitstreams/72a8909d-2214-4aaf-9e72-66ee665260eb/download 9c9a5e173dc9d2d5e30015a061b4abc8 https://oarep.usim.edu.my/bitstreams/f677d69e-1e4a-4cb7-a133-6d3e1d08b2df/download 7cc42f3238ce564cf06fd2b3b018ea51 https://oarep.usim.edu.my/bitstreams/39f4338c-3156-4a05-87ae-297b1031a4ac/download 358e64f53c4abab6f727d89dfff6a8fe https://oarep.usim.edu.my/bitstreams/3978a7fb-f268-436f-94bc-bad942bffd51/download 6cde0cc70ba01a6f0ea2232efbbd7f28 https://oarep.usim.edu.my/bitstreams/c9993481-39ab-460d-91b9-787cacd51ea2/download 3fbd0eca095559aebfff27912627e152 Leukaemia Microscopic image data Computer applications -- Clinical pathology Medical technology |