In modern world, we have to deal with huge volumes of data which include image, video, text and web documents, DNA, microarray gene data, etc. Organizing such data into rational groups is a critical first step to draw inferences. Data clustering analysis has emerged as an effective technique to accurately accomplish the task of categorizing data into sensible groups. Clustering has a rich association with researches in various scientific domains. One of the most popular clustering algorithms, k-means algorithm was proposed as early as 1957. Since then, many clustering algorithms have been developed and used, to group data in various commercial and non-commercial sectors alike. In this paper, we have given concise description of the existing types of clustering approaches followed by a survey of the fields where clustering analytics has been effectively employed in pattern recognition and knowledge discovery.

A Short Review on Different Clustering Techniques and Their Applications

Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method’s sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm’s stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.

/pdf/brain-tumor-segmentation-based-on-hybrid-clustering-and-4usetpg3wi.pdf

Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations.

Lattice-valued overlap and quasi-overlap functions

Traditional fuzzy clustering algorithms suffer from two problems in image segmentations. One is that these algorithms are sensitive to outliers due to the non-sparsity of fuzzy memberships. The other is that these algorithms often cause image over-segmentation due to the loss of image local spatial information. To address these issues, we propose a robust self-sparse fuzzy clustering algorithm (RSSFCA) for image segmentation. The proposed RSSFCA makes two contributions. The first concerns a regularization under Gaussian metric that is integrated into the objective function of fuzzy clustering algorithms to obtain fuzzy membership with sparsity, which reduces a proportion of noisy features and improves clustering results. The second concerns a connected-component filtering based on area density balance strategy (CCF-ADB) that is proposed to address the problem of image over-segmentation. Compared to the integration of local spatial information into the objective functions, the presented CCF-ADB is simpler and faster for the removal of small areas. Experimental results show that the proposed RSSFCA addresses two problems in current fuzzy clustering algorithms, i.e., the outlier sensitivity and the over-segmentation, and it provides better image segmentation results than state-of-the-art algorithms.

/pdf/robust-self-sparse-fuzzy-clustering-for-image-segmentation-ta5jwwfb5m.pdf

Robust Self-Sparse Fuzzy Clustering for Image Segmentation

Detection of spinal cord injury (SCI) is one of the major problems in MRI images to detect the affected portion of spinal cord regions using feature sets. Automatic detection of spinal cord atrophy is complex due to change in structure, size, and white matter. Delineating gray matter and white matter are the essential factors that influence the detection of spinal cord atrophy and its severity. Automatic segmentation and classification are accurate methods for detecting the severity of the SCI. Hierarchical segmentation, partitioning segmentation, graph, and watershed segmentation methods are used to find the SCI segments in static fixed positions. Also, these segmentation models result in a high false positive rate due to over segmentation features and noise in the segmented regions. Furthermore, these classification methods fail to segment and detect the severity level in the affected region due to over segmentation. In order to overcome these issues, a novel segment-based classification model is required to find the severity of the injury and to predict the disease patterns on the over segmented regions and features. In the present model, a hybrid image threshold technique is used to segment the spinal cord regions for non-linear SVM classification approach. Among the traditional feature segmentation-based classification models, the proposed threshold-based non-linear SVM has better accuracy for SCI detection. The results proved that the present model is more efficient than the earlier approaches in terms of true positive rate ( $\text {TP}=0.9783$ ) and accuracy (0.9683).

/pdf/fast-and-accurate-feature-extraction-based-segmentation-1w018b8ko1.pdf

Fast and Accurate Feature Extraction-Based Segmentation Framework for Spinal Cord Injury Severity Classification

Integrating guided filter into fuzzy clustering for noisy image segmentation

Fuzzy clustering generates a membership vector for each data point in the dataset to indicate its belongingness to different clusters. This procedure can be regarded as an encoding process and the obtained vectors of memberships are the new representations of original data. Naturally, the affinities between new representations or the vectors of memberships should be consistent with the ones between original data points. For example, the data points close to each other should also take similar membership vectors. Such constraints on the affinities of memberships are valuable prior knowledge that should be imposed to the objective function of fuzzy clustering for better performance. To this end, we introduce the membership affinity lasso for fuzzy clustering in this paper. Utilizing alternating direction method of multipliers, an efficient approach is derived to optimize the general membership affinity lasso regularized fuzzy clustering model in offline manner. As illustrative examples, three new fuzzy clustering algorithms with the membership affinity lasso are proposed. Experiments on the synthetic and real data demonstrate the superiority and flexibility of the proposed algorithms.

Membership Affinity Lasso for Fuzzy Clustering

Pixel and region level information fusion in membership regularized fuzzy clustering for image segmentation

Ensemble clustering combines different basic partitions of a dataset into a more stable and robust one. Thus, cluster ensemble plays a significant role in applications like image segmentation. However, existing ensemble methods have a few demerits, including the lack of diversity of basic partitions and the low accuracy caused by data noise. In this paper, to get over these difficulties, we propose an efficient fuzzy cluster ensemble method based on Kullback–Leibler divergence or simply, the KL divergence. The data are first classified with distinct fuzzy clustering methods. Then, the soft clustering results are aggregated by a fuzzy KL divergence-based objective function. Moreover, for image segmentation problems, we utilize the local spatial information in the cluster ensemble algorithm to suppress the effect of noise. Experiment results reveal that the proposed methods outperform many other methods in synthetic and real image-segmentation problems.

https://www.mdpi.com/1099-4300/20/4/273/pdf

KL Divergence-Based Fuzzy Cluster Ensemble for Image Segmentation

Prior knowledge has been considered as valuable supplementary information in many image processing techniques. In this paper, we take the input image itself as the guidance prior and develop a novel fuzzy clustering algorithm to segment it by adding a new term to the objective function of Fuzzy C-Means. The new term comes from Guided Filter for its capability in noise suppression and edge-preserving smoothing. As a result, the memberships derived from the new objective function incorporate the guidance information from the image to be segmented. In this way, the segmentation result retains more subtle details on the boundaries of segments. According to experimental results, the proposed method shows good performance in image segmentation tasks especially for images with high noise rates.

Image Guided Fuzzy C-Means for Image Segmentation

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