Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578188
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSeyed Mostafa Mousavi Kahaki (UKM)
dc.contributor.authorMd Jan Nordin (UKM)
dc.contributor.authorSophia Jamila Zahra (UKM)
dc.date.accessioned2023-11-06T02:59:02Z-
dc.date.available2023-11-06T02:59:02Z-
dc.date.issued2017-06
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:113372
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578188-
dc.descriptionBlood cancer is an umbrella term for cancers that affect the blood, bone marrow and lymphatic system. There are three main groups of blood cancer: leukemia, lymphoma and myeloma. Some types are more common than others. In this paper, a new image transform based on geometric mean properties of integral values in both horizontal and vertical image directions is proposed for leukemia cancer cell classification. Available classification methods using the classical feature extraction methods which are sensitive to rotation and deformation of the blood cells. The new transform is based on geometric mean projection, which —unlike other image transforms, such as Radon transform— is not considered all signals in an image with the same signal acquisition rate. Instead, it is general and thus applicable to all capturing signal functions to achieve sufficient invariant features. The geometric mean projection transforms (GMPT) guarantees that the detector only extracts the highly informative information from the object to achieve an invariant feature vector for an accurate classification process. This method has been used as cancer cell identification using microscopic Imagery analysis in this study. Dissimilarity metric calculation and shape analysis by using image transform has been used to extract the feature vectors of the imagery. Then, the accumulated feature vectors have been classified to different classes by using artificial neural network (ANN). The proposed technique has been evaluated in the standard images sourced from USIM, Malaysia. The evaluation results indicate the robustness of the technique in different types of images available in the dataset.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/view_archives.php?journal=JST-25-S-6
dc.rightsUKM
dc.subjectCancer cell classification Image transform
dc.subjectImage processing
dc.subjectPattern recognition
dc.titleBlood cancer cell classification based on geometric mean transform and dissimilarity metrics
dc.typeJournal Article
dc.format.volume25
dc.format.pages223-234
dc.format.issueSpecial Issue
Appears in Collections:Journal Content Pages/ Kandungan Halaman Jurnal

Files in This Item:
File Description SizeFormat 
ukmvital_113372+Source01+Source010.PDF302.48 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.