Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization. (August 2021)

• Record Type: Journal Article
• Authors: Patra, Dipak Kumar; Si, Tapas; Mondal, Sukumar; Mukherjee, Prakash
• Is Part Of: Biomedical signal processing and control. Volume 69(2021)
• Publication Date: 2021-08
• Subjects: Breast cancer -- Lesions -- DCE-MRI -- Segmentation -- Entropy -- Multi-level thresholding -- Student Psychology Based Optimizer; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102925
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A Multi-level thresholding-based breast DCE-MRI segmentation for lesion detection. Application of Student Psychological-Based Optimizer (SPBO) in entropy maximization. Proposed method performs better than eight compared methods. Abstract: In recent years, the high prevalence of breast cancer in women has risen dramatically. Therefore, segmentation of breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a necessary task to assist the radiologist inaccurate diagnosis and detection of breast cancer in breast DCE-MRI. For image segmentation, thresholding is a simple and effective method. In breast DCE-MRI analysis for lesion detection and segmentation, radiologists agree that optimization via multi-level thresholding technique is important to differentiate breast lesions from dynamic DCE-MRI. In this paper, multi-level thresholding using Student Psychology-Based Optimizer (SPBO) is proposed to segment the breast DCE-MR images for lesion detection. First, MR images are denoised using the anisotropic diffusion filter and then, Intensity Inhomogeneities (IIHs) are corrected in the preprocessing step. The preprocessed MR images are segmented using the SPBO algorithm. Finally, the lesions are extracted from the segmented images and localized in the original MR images. The proposed method is applied on 300 Sagittal T2-Weighted DCE-MRI slices of 50 patients, histologically proven, and analyzed. The proposed method is compared with algorithms such as Particle Swarm Optimizer (PSO), Dragonfly Optimization (DA), Slime Mould Optimization (SMA), Multi-Verse Optimization (MVO), Grasshopper Optimization Algorithm (GOA), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF) methods. The high accuracy level of 99.44 %, sensitivity 96.84 %, and Dice Similarity Coefficient (DSC) 93.41 % are achieved using the proposed automatic segmentation method. Both quantitative and qualitative results demonstrate that the proposed method performs better than the eight compared methods.