Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

Abstract

Segmentation of brain tumor images, to refine the detection and understanding of abnormal masses in the brain, is an important research topic in medical imaging. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed, to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied, to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block, to classify it as a tumor or not. A thorough evaluation of the proposed algorithm was performed, using: (1) a private MRI database provided by the Kouba imaging center—Algiers (KICA); (2) the multimodal brain tumor segmentation challenge (BraTS) 2015 database. Estimates of the selected fitness function were first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion, to demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm was then compared to the results of several state-of-the-art techniques. The results obtained, by using the Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements, demonstrated the superiority of the proposed optimized segmentation algorithm over equivalent techniques.

Comparison of Multiparametric MRI, [68Ga]Ga-PSMA-11 PET-CT, and Clinical Nomograms for Primary T and N Staging of Intermediate-to-High-Risk Prostate Cancer

“…These allow the human factor to be reduced, for example, when recognizing and naming brain lesions [46]. There is also emerging work on the analysis of MRI images of the prostate allowing automatic segmentation and the determination of the extent of disease [47]. For PET-CT examinations, neural networks are able to classify physiological tracer accumulation in organs [48], and there is also emerging work on machine-learning-based analysis of quantitative [ 18 F]DCFPyL PET-CT in the prediction of LNI and high-risk pathological tumor features in primary PCa patients [49].…”

Efficient and Automatic Breast Cancer Early Diagnosis System Based on the Hierarchical Extreme Learning Machine

“…Then, another software, named Fast Photo Crop, is used, to obtain ultrasound images with similar sizes, of pixels, or this part can be replaced by other tools to generalize the images, such as Matlab. Moreover, to improve the efficiency and accuracy of training and processing, the images are segmented further, because in the biomedical engineering area, image segmentation is the action of grouping pixels according to predefined criteria, in order to build regions or classes of pixels [ 41 ]. Therefore, considering the powerful learning abilities of the H-ELM, the images are further simplified from gray images into logic images, and reduce the sizes one more time.…”

A Review of Document Binarization: Main Techniques, New Challenges, and Trends

“…Image binarization is an important aspect of image analysis, such as scene text detection [1][2][3] and medical image analysis [4,5]. Especially in the field of document image processing, binarization has a wide range of applications as a basic method of digital image processing, including text recognition, document image segmentation, image morphological processing, and feature extraction [6][7][8][9].…”