Performance and Robustness of Regional Image Segmentation Driven by Selected Evolutionary and Genetic Algorithms: Study on MR Articular Cartilage Images

Abstract

The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur’s entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation’s robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.

Efficient Approach to Color Image Segmentation Based on Multilevel Thresholding Using EMO Algorithm by Considering Spatial Contextual Information

“…(v) Feature similarity (FSIM), this is similar to SSIM, which indicates degradation of image quality; it ranges [−1, 1]; a high value of FSIM means better segmentation of the color image. (vi) probability Rand index (PRI) or simply Rand index (RI), this computes the connection between the ground truth and segmented image; better performance [9,42,43] is indicated by a higher PRI value. (vii) Variation of information (VOI), this gives the randomness of a segmented image; a low VOI value indicates better segmentation performance.…”

“…The segmented images with various optimization techniques are obtained from published articles and this study proves that the proposed approach provides better performance [44,45] than the techniques considered in this research work. Figures 2 and 3 illustrate the segmented results using the proposed (MTEMOE) approach to color image segmentation based on Otsu's and Kapur's methods [43,46,47]. In the end, a statistical analysis is firmly used to demonstrate the dominance of the proposed approach.…”

Brain MRA 3D Skeleton Extraction Based on Normal Plane Centroid Algorithm

“…There are also algorithms based on specific tools (segmentation algorithm based on clustering, segmentation algorithm based on wavelet transform, segmentation algorithm based on active contour model, genetic algorithm, etc.) [17][18][19][20] and algorithms based on deep learning [8,[21][22][23], etc. Deep learning algorithms are also subdivided into various algorithms.…”

A differential evolutionary algorithm for multi-threshold image segmentation based on adaptive parameter control strategy

“…To extract the target part of interest in an image for further processing and computer vision research, digital image segmentation techniques are used. The most popular image segmentation techniques include region image segmentation [1], maximum entropy [2]- [5], edge detection image segmentation [6], and threshold image segmentation [7], [8]. There are also some unique thresholding techniques, such as that proposed by Patra [9], which makes use of an energy function to build an image's energy profile while considering contextual data in the image space to determine the ideal threshold.…”

“…Consequently, selecting thresholds for picture segmentation has become a prominent use of the maximum interclass variance method. The greatest interclass variance method has been applied successfully to the segmentation of canopy and medical images [21], [1].…”