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Preliminary studies for detection of penicillium species using adaptive histogram equalization technique

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cris.author.scopus-author-id 57212316655
cris.author.scopus-author-id 55357649900
cris.author.scopus-author-id 57212305974
cris.author.scopus-author-id 57219027157
dc.contributor.author Zabani F.N.
dc.contributor.author Jaafar H.
dc.contributor.author Radzuan N.R.R.M.
dc.contributor.author Nasir A.S.A.
dc.date.accessioned 2024-12-12T08:15:56Z
dc.date.available 2024-12-12T08:15:56Z
dc.date.issued 2019-10-01
dc.description.abstract This paper proposes an image analysis scheme to automatically detect the morphological feature of a fungi, Penicillium. While the previous method of detection is rather difficult as it is time consuming and only an experienced mycologist can evaluate the fungi cultures, the proposed method aims to ease the works of mycologists and shorten the time of detection. The image of fungi usually suffers from a low contrast and presence of noise which makes it difficult to detect the morphological features. Therefore, an application of sharpening filter together with adaptive histogram equalization was investigated in the image enhancement stage to overcome the low quality Penicillium image. To eliminate the noise, a combined operation of Canny edge detection algorithm, morphological operation and largest connected pixel region was applied on the image. To reduce the time consumption, only the morphological features of the fungi is segmented in this study. The resulted image from each process of enhancement and segmentation is then evaluated using PSNR. The results shows Laplacian filter outperform AHE with a value of 54. 105dB.
dc.identifier.doi 10.1109/ICSEngT.2019.8906499
dc.identifier.isbn [9781728107585]
dc.identifier.scopus 2-s2.0-85076396769
dc.identifier.uri https://hdl.handle.net/20.500.14170/10199
dc.relation.grantno undefined
dc.relation.ispartof 2019 IEEE 9th International Conference on System Engineering and Technology, ICSET 2019 - Proceeding
dc.relation.ispartofseries 2019 IEEE 9th International Conference on System Engineering and Technology, ICSET 2019 - Proceeding
dc.subject AHE | Detection | Image enhancement | Morphological features | Penicillium
dc.title Preliminary studies for detection of penicillium species using adaptive histogram equalization technique
dc.type Conference Proceeding
dspace.entity.type Publication
oaire.citation.endPage 240
oaire.citation.startPage 236
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.citation.number 8906499
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person.identifier.scopus-author-id 57212316655
person.identifier.scopus-author-id 55357649900
person.identifier.scopus-author-id 57212305974
person.identifier.scopus-author-id 57219027157
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