Spectrally distinct pixel extraction and kernel filtering for brain tumour diagnosis

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dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorAlsubai, Shtwai
dc.contributor.authorAseeri, Ahmad O.
dc.contributor.authorNandal, Amita
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-09-25T19:56:25Z
dc.date.available2024-09-25T19:56:25Z
dc.date.issued2024
dc.departmentAbant İzzet Baysal Üniversitesien_US
dc.description.abstractBefore surgery, medical segmentation is carried out to ascertain the limits of regions of interest (ROI). Critical information may be gathered during the planning phase of the operation if it is permitted that the growth, structure, and behaviour of the ROI be studied. This will increase the probability that the operation will be successful. Most of the time, segmentations are carried out either manually or via machine learning algorithms based on hand annotations. The difference between pure background information and an approximation of such information would affect the performance of brain tumour detection algorithms. This paper offers a technique for the identification of tumour regions that is based on kernel background filtration. The proposed approach presented has a more precise estimation of background information as its primary objective. It is composed of four stages. The first stage includes a probabilistic method based on image denoising. The second stage includes extracting the pure background pixels using a kernel-based technique. The third stage estimates the background covariance matrix using the pixels taken from the pure background pixels. In the fourth phase, we perform segmentation using DCNN. The segmentation procedure begins with training a deep convolutional neural network (DCNN) to recreate damaged brain regions. Window-based methods identify pixels with the largest reconstruction loss. This study isolates tumorous regions using pixel-wise segmentation. The proposed method segments tumours of various sizes with an average Dice score of 0.82.en_US
dc.description.sponsorshipPrince Sattam bin Abdulaziz University [PSAU/2024/R/1445]en_US
dc.description.sponsorshipThis study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2024/R/1445) .en_US
dc.identifier.doi10.1016/j.bspc.2024.106787
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85202343071en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2024.106787
dc.identifier.urihttps://hdl.handle.net/20.500.12491/13289
dc.identifier.volume98en_US
dc.identifier.wosWOS:001313232200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing And Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectBackground filteringen_US
dc.subjectCNNen_US
dc.subjectDenoisingen_US
dc.subjectDice scoreen_US
dc.subjectKernel masken_US
dc.subjectSegmentationen_US
dc.subjectSpectral banden_US
dc.titleSpectrally distinct pixel extraction and kernel filtering for brain tumour diagnosisen_US
dc.typeArticleen_US

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