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

• Published in: Journal of Information and Communication Technology
• Main Author: Azman N.F.; Jumaat A.K.; Azam A.S.B.; MohdGhani N.A.S.; Maasar M.A.; Laham M.F.; Rahman N.N.A.
• Format: Article
• Language: English
• Published: Universiti Utara Malaysia Press 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200114995&doi=10.32890%2fjict2024.23.3.2&partnerID=40&md5=1fb566a921f8d0da8201c93defc1595a

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. © (2023), (Universiti Utara Malaysia Press). All Rights Reserved.