title: Automated nuclei segmentation of malignant using level sets.
abstract: Segmentation of objects from a noisy and complex image is still a challenging task that needs to be addressed. This article proposed a new method to detect and segment nuclei to determine whether they are malignant or not (determination of the region of interest, noise removal, enhance the image, candidate detection is employed on the centroid transform to evaluate the centroid of each object, the level set [LS] is applied to segment the nuclei). The proposed method consists of three main stages: preprocessing, seed detection, and segmentation. Preprocessing stage involves the preparation of the image conditions to ensure that they meet the segmentation requirements. Seed detection detects the seed point to be used in the segmentation stage, which refers to the process of segmenting the nuclei using the LS method. In this research work, 58 H&E breast cancer images from the UCSB Bio-Segmentation Benchmark dataset are evaluated. The proposed method reveals the high performance and accuracy in comparison to the techniques reported in literature. The experimental results are also harmonized with the ground truth images.
authors.affiliation: Faculty of Computing, Universiti Teknologi, Johor Bahru, Malaysia.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Husham
authors.affiliation: Faculty of Information Sciences & Engineering, Management and Science University, Shah Alam, Selangor, Malaysia.|Faculty of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Hazim Alkawaz
authors.affiliation: College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
authors.forename: Tanzila
authors.initials: T
authors.lastname: Saba
authors.affiliation: College of Computer and Information Systems, Al-Yamamah University, Riyadh, Saudi Arabia. a_khan@yu.edu.sa.
authors.forename: Amjad
authors.initials: A
authors.lastname: Rehman
authors.affiliation: College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
authors.forename: Jarallah
authors.initials: J
authors.lastname: Saleh Alghamdi
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002467:Cell Nucleus / Q000648:ultrastructure*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*
keywords: histopathology; level sets; malignant detection; nuclei; segmentation