Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation
Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. However, two main issues plague these clustering algorithms: initialization sensitivity of cluster centers and unknown number of actual clusters in the given...
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| Format: | Thesis |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.usm.my/40908/ http://eprints.usm.my/40908/1/Osama_24MS_HJ.pdf |
| _version_ | 1848879149945454592 |
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| author | Alia, Osama Moh’d Radi |
| author_facet | Alia, Osama Moh’d Radi |
| author_sort | Alia, Osama Moh’d Radi |
| building | USM Institutional Repository |
| collection | Online Access |
| description | Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among
the most successful methods for image segmentation. However, two main issues plague these
clustering algorithms: initialization sensitivity of cluster centers and unknown number of actual
clusters in the given dataset. This thesis aims to solve these problems using an efficient
metaheuristic algorithm, known as the Harmony Search (HS) algorithm. First, two alternative
HS-based fuzzy clustering methods are proposed. The aim of these methods is to overcome
the limitation faced by conventional fuzzy clustering algorithms, which are known to provide
sub-optimal clustering depending on the choice of the initial clusters. Second, a new dynamic
HS-based fuzzy clustering algorithm (DCHS) is proposed to automatically estimate the appropriate
number of clusters as well as a good fuzzy partitioning of the given dataset. These
algorithms have been applied to the problem of image segmentation. Various images from
different application domains, including synthetic and real-world images, have been used in
this thesis to show the applicability of the proposed algorithms. Finally, the proposed DCHS
algorithm is applied to two real-world medical image problems, namely, malignant bone tumour
(osteosarcoma) and magnetic resonance imaging brain segmentation. The experimental
results are very promising showing significant improvements compared to other approaches in
the same domain. |
| first_indexed | 2025-11-15T17:42:39Z |
| format | Thesis |
| id | usm-40908 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T17:42:39Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-409082018-07-05T02:35:27Z http://eprints.usm.my/40908/ Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation Alia, Osama Moh’d Radi QA1 Mathematics (General) Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. However, two main issues plague these clustering algorithms: initialization sensitivity of cluster centers and unknown number of actual clusters in the given dataset. This thesis aims to solve these problems using an efficient metaheuristic algorithm, known as the Harmony Search (HS) algorithm. First, two alternative HS-based fuzzy clustering methods are proposed. The aim of these methods is to overcome the limitation faced by conventional fuzzy clustering algorithms, which are known to provide sub-optimal clustering depending on the choice of the initial clusters. Second, a new dynamic HS-based fuzzy clustering algorithm (DCHS) is proposed to automatically estimate the appropriate number of clusters as well as a good fuzzy partitioning of the given dataset. These algorithms have been applied to the problem of image segmentation. Various images from different application domains, including synthetic and real-world images, have been used in this thesis to show the applicability of the proposed algorithms. Finally, the proposed DCHS algorithm is applied to two real-world medical image problems, namely, malignant bone tumour (osteosarcoma) and magnetic resonance imaging brain segmentation. The experimental results are very promising showing significant improvements compared to other approaches in the same domain. 2011-02 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/40908/1/Osama_24MS_HJ.pdf Alia, Osama Moh’d Radi (2011) Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA1 Mathematics (General) Alia, Osama Moh’d Radi Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation |
| title | Harmony Search-Based Fuzzy
Clustering Algorithms For Image
Segmentation
|
| title_full | Harmony Search-Based Fuzzy
Clustering Algorithms For Image
Segmentation
|
| title_fullStr | Harmony Search-Based Fuzzy
Clustering Algorithms For Image
Segmentation
|
| title_full_unstemmed | Harmony Search-Based Fuzzy
Clustering Algorithms For Image
Segmentation
|
| title_short | Harmony Search-Based Fuzzy
Clustering Algorithms For Image
Segmentation
|
| title_sort | harmony search-based fuzzy
clustering algorithms for image
segmentation |
| topic | QA1 Mathematics (General) |
| url | http://eprints.usm.my/40908/ http://eprints.usm.my/40908/1/Osama_24MS_HJ.pdf |