Fast Unsupervised Bayesian Image Segmentation With Adaptive Spatial Regularisation

Restored texture segmentation using Markov random fields

Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a huge dataset and many others. Motivated by the latest work on noisy textures, during our work being presented here, Brodatz and Prague texture images are contaminated with Gaussian and salt-n-pepper noise. A three-phase approach is developed for the segmentation of textures contaminated by noise. In the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature. In the remaining two phases, segmentation of the restored textures is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the Median Filter based on segmentation performance metrics. When the proposed approach is evaluated on Brodatz textures, an improvement of up to 16% segmentation accuracy for salt-n-pepper noise with 70% noise density and 15.1% accuracy for Gaussian noise (with a variance of 50) has been made in comparison with the benchmark approaches. On Prague textures, accuracy is improved by 4.08% for Gaussian noise (with variance 10) and by 2.47% for salt-n-pepper noise with 20% noise density. The approach in the present study can be applied to a diversified class of image analysis applications spanning a wide spectrum such as satellite images, medical images, industrial inspection, geo-informatics, etc.

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

During the COVID-19 pandemic, huge interstitial lung disease (ILD) lung images have been captured. It is high time to develop the efficient segmentation techniques utilized to separate the anatomical structures and ILD patterns for disease and infection level identification. The effectiveness of disease classification directly depends on the accuracy of initial stages like preprocessing and segmentation. This paper proposed a hybrid segmentation algorithm designed for ILD images by taking advantage of superpixel and K-means clustering approaches. Segmented superpixel images adapt the better irregular local and spatial neighborhoods that are helpful to improving the performance of K-means clustering-based ILD image segmentation. To overcome the limitations of multiclass belongings, semiadaptive wavelet-based fusion is applied over selected K-means clusters. The performance of the proposed SPFKMC was compared with that of 3-class Fuzzy C-Means clustering (FCM) and K-Means clustering in terms of accuracy, Jaccard similarity index, and Dice similarity coefficient. The SPFKMC algorithm gives an accuracy of 99.28%, DSC 98.72%, and JSI 97.87%. The proposed Fused Clustering gives better results as compared to traditional K-means clustering segmentation with wavelet-based fused cluster results.

A Marker-Controlled Watershed Algorithm for the Intelligent Picking of Long Jujubes in Trees

Vision is the most important way for an unmanned picking or plant protection robot to navigate an external environment. To achieve intelligent picking or plant protection, it is essential to obtain target location information. A new marker-controlled watershed (MCW-D) algorithm is proposed for object segmentation. By analyzing the shortcomings of the watershed algorithm and the characteristics of objects, the proposed MCW-D method mainly solves three problems. First, it reduces the influence of shadow and other factors on image color information. Based on histogram specification, secondary mapping is used to reduce the effects of lighting. Second, marker images are selected. All points with markers need to be located in the target object. The hue feature of long jujubes and trees is used as the marker image. Third, a mask image is acquired, which requires a clear boundary between the target and the background. An adaptive angle rotation based on an energy-driven approach is designed to find large differences between the target and the background. In a natural environment, the proposed MCW-D method respectively achieves segmentation accuracies of 94.7% and 93.2% on a jujube dataset and a tree dataset, which exceed the accuracies of widely used machine learning methods. These results promote the development of the forest and fruit economies.

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability since the Kullback–Leibler (KL) divergence of the noise smoothing factor is used to supervise the prior probability; this probability is embedded into Fuzzy Superpixels Fuzzy C-means (FSFCM) as a regular factor. This paper proposes a fuzzy clustering image segmentation algorithm based on an adaptive feature selection Gaussian mixture model with neighbourhood information constraints. To verify the segmentation performance and anti-noise robustness of the improved algorithm, the fuzzy C-means clustering algorithm Fuzzy C-means (FCM), FSFCM, Spatially Variant Finite Mixture Model (SVFMM), EGFMM, extended Gaussian mixture model (EGMM), adaptive feature selection robust fuzzy clustering segmentation algorithm (AFSFCM), fast and robust spatially constrained Gaussian mixture model (GMM) for image segmentation (FRSCGMM), and improve method are used to segment grey images containing Gaussian noise, salt-and-pepper noise, multiplicative noise and mixed noise. The peak signal-to-noise ratio (PSNR) and the error rate (MCR) are used as the theoretical basis for assessing the segmentation results. The improved algorithm indicators proposed in this paper are optimised. The improved algorithm yields increases of 0.1272–12.9803 dB, 1.5501–13.4396 dB, 1.9113–11.2613 dB and 1.0233–10.2804 dB over the other methods, and the Misclassification rate (MSR) decreases by 0.32–37.32%, 5.02–41.05%, 0.3–21.79% and 0.9–30.95% compared to that with the other algorithms. It is verified that the segmentation results of the improved algorithm have good regional consistency and strong anti-noise robustness, and they meet the needs of noisy image segmentation.

A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model

Since the fuzzy local information C-means (FLICM) segmentation algorithm cannot take into account the impact of different features on clustering segmentation results, a local fuzzy clustering segmentation algorithm based on a feature selection Gaussian mixture model was proposed. First, the constraints of the membership degree on the spatial distance were added to the local information function. Second, the feature saliency was introduced into the objective function. By using the Lagrange multiplier method, the optimal expression of the objective function was solved. Neighborhood weighting information was added to the iteration expression of the classification membership degree to obtain a local feature selection based on feature selection. Each of the improved FLICM algorithm, the fuzzy C-means with spatial constraints (FCM_S) algorithm, and the original FLICM algorithm were then used to cluster and segment the interference images of Gaussian noise, salt-and-pepper noise, multiplicative noise, and mixed noise. The performances of the peak signal-to-noise ratio and error rate of the segmentation results were compared with each other. At the same time, the iteration time and number of iterations used to converge the objective function of the algorithm were compared. In summary, the improved algorithm significantly improved the ability of image noise suppression under strong noise interference, improved the efficiency of operation, facilitated remote sensing image capture under strong noise interference, and promoted the development of a robust anti-noise fuzzy clustering algorithm.

High-Level Synthesis of Online K-Means Clustering Hardware for a Real-Time Image Processing Pipeline

The growing need for smart surveillance solutions requires that modern video capturing devices to be equipped with advance features, such as object detection, scene characterization, and event detection, etc. Image segmentation into various connected regions is a vital pre-processing step in these and other advanced computer vision algorithms. Thus, the inclusion of a hardware accelerator for this task in the conventional image processing pipeline inevitably reduces the workload for more advanced operations downstream. Moreover, design entry by using high-level synthesis tools is gaining popularity for the facilitation of system development under a rapid prototyping paradigm. To address these design requirements, we have developed a hardware accelerator for image segmentation, based on an online K-Means algorithm using a Simulink high-level synthesis tool. The developed hardware uses a standard pixel streaming protocol, and it can be readily inserted into any image processing pipeline as an Intellectual Property (IP) core on a Field Programmable Gate Array (FPGA). Furthermore, the proposed design reduces the hardware complexity of the conventional architectures by employing a weighted instead of a moving average to update the clusters. Experimental evidence has also been provided to demonstrate that the proposed weighted average-based approach yields better results than the conventional moving average on test video sequences. The synthesized hardware has been tested in real-time environment to process Full HD video at 26.5 fps, while the estimated dynamic power consumption is less than 90 mW on the Xilinx Zynq-7000 SOC.

Detection of retinal pigment epithelium detachment from OCT images using multiscale Gaussian filtering

Macular diseases, including neovascular age-related macular degeneration (nvAMD), are leading causes of irreversible blindness and visual impairment. One prominent feature of nvAMD is the detachment of the retinal pigment epithelium. The aim of this study is to implement an automated method for the segmentation of the pigment epithelial detachment (PED) using optical coherence tomography (OCT). OCT datasets from 8 patients with nvAMD were acquired during multiple sessions. At each session, 17 images with a resolution of 1020 × 640 pixels were obtained. The images were segmented using Gaussian filtering and template matching for the detection of the upper and lower border of the PED, respectively. The results of the method were compared with the ones obtained from the manual segmentation of the images by an expert. Four well-known metrics were used to evaluate the performance of the method with respect to the manual segmentation, resulting in high scores of consistency. Furthermore, the proposed method was also compared with four other well-known methods providing similar or superior performance.

Random-Reaction-Seed Method for Automated Identification of Neurite Elongation and Branching

Conventional deterministic algorithms (i.e., skeletonization and edge-detection) lack robustness and sensitivity to reliably detect the neurite elongation and branching of low signal-to-noise-ratio microscopy images. Neurite outgrowth experiments produce an enormous number of images that require automated measurement; however, the tracking of neurites is easily lost in the automated process due to the intrinsic variability of neurites (either axon or dendrite) under stimuli. We have developed a stochastic random-reaction-seed (RRS) method to identify neurite elongation and branching accurately and automatically. The random-seeding algorithm of RRS is based on the hidden-Markov-model (HMM) to offer a robust enough way for tracing arbitrary neurite structures, while the reaction-seeding algorithm of RRS secures the efficiency of random seeding. It is noteworthy that RRS is capable of tracing a whole neurite branch by only one initial seed, so that RRS is proficient at quantifying extensive amounts of neurite outgrowth images with noisy background in microfluidic devices of biomedical engineering fields. The method also showed notable performance for reconstructing of net-like structures, and thus is expected to be proficient for biomedical feature extractions in a wide range of applications, such as retinal vessel segmentation and cell membrane profiling in spurious-edge-tissues.