Digital Medical Images Segmentation by Active Contour Model based on the Signed Pressure Force Function

Authors

  • Nor Farihah Azman School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Malaysia
  • Abdul Kadir Jumaat School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Malaysia and Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Malaysia
  • Akmal Shafiq Badarul Azam School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Sarawak Branch, Mukah Campus, Malaysia
  • Noor Ain Syazwani Mohd Ghani School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Malaysia
  • Mohd Azdi Maasar Mathematical Sciences Studies, College of Computing, Informatics and Mathematics, Seremban Campus, Universiti Teknologi MARA (UiTM), Negeri Sembilan Branch, Malaysia
  • Mohamed Faris Laham Institute for Mathematical Research, Universiti Putra Malaysia, Malaysia
  • Normahirah Nek Abd Rahman Pusat GENIUS@Pintar Negara, Universiti Kebangsaan Malaysia, Malaysia

DOI:

https://doi.org/10.32890/jict2024.23.3.2

Keywords:

Active contour model, selective segmentation, signed pressure force function, intensity inhomogeneity, noise

Abstract

The signed pressure force (SPF) function has recently become a popular function for guiding the curve evolution of the active contour model (ACM) for image segmentation. The aim is to extract the boundaries of digital medical images for shape and image analysis. The recent SPF-based ACM demonstrates effectiveness in image segmentation. However, it may fail if the targeted object is close to a neighbouring object. Additionally, the presence of intensity inhomogeneity and noise in medical images degrades segmentation accuracy and local target areas. Thus, we proposed a new SPF-based ACM, namely the Selective Segmentation with Signed Pressure Force 1 (SSPF1) model, by incorporating the ideas of the SPF function and the distance fitting term based on geometrical constraints. Then, the new SSPF1 model was extended by incorporating an image enhancement technique to develop our second new model, termed the Selective Segmentation with Signed Pressure Force 2 (SSPF2). Numerical results indicated that the SSPF2 model was more recommended than SSPF1 as the SSPF2 model was approximately 4.7% more accurate, as indicated by the Jaccard value and was about 112 times faster in segmenting noisy images compared to the existing selective segmentation model. 

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Published

28-07-2024

Issue

Vol. 23 No. 3 (2024): Journal of Information and Communication Technology (JICT) Vol.23, No.3, July 2024

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Articles

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How to Cite

Nor Farihah Azman, Abdul Kadir Jumaat, Akmal Shafiq Badarul Azam, Noor Ain Syazwani Mohd Ghani, Mohd Azdi Maasar, Mohamed Faris Laham, & Normahirah Nek Abd Rahman. (2024). Digital Medical Images Segmentation by Active Contour Model based on the Signed Pressure Force Function. Journal of Information and Communication Technology, 23(3), 393-419. https://doi.org/10.32890/jict2024.23.3.2

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Harvested 2026-09-06
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Identifiers DOI 10.32890/jict2024.23.3.2 OpenAlex W4401102236 Scopus 85200114995