scholarly journals Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 188 ◽  
Author(s):  
Hannah Inbarani H. ◽  
Ahmad Taher Azar ◽  
Jothi G
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Diagnostic System ◽  
Histogram Method ◽  
Prediction Algorithms ◽  
Segmentation Methods ◽  
Random Initialization ◽  
Occurrence Matrix ◽  
Covering Rough Set

Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-means clustering. The histogram method was utilized to identify the number of clusters to avoid random initialization. Different types of features such as gray level co-occurrence matrix (GLCM), color, and shape-based features were extracted from the segmented image of the nucleus. Machine learning prediction algorithms were applied to classify the cancerous and non-cancerous cells. The proposed strategy is compared with an existing clustering algorithm, and the efficiency is evaluated based on the prediction metrics. The experimental results show that the HSCRKM method efficiently segments the nucleus, and it is also inferred that logistic regression and neural network perform better than other prediction algorithms.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

Texture Image Segmentation Using Affinity Propagation and Spectral Clustering

2015 ◽  
Vol 29 (05) ◽  
pp. 1555009 ◽  
Author(s):  
Hui Du ◽  
Yuping Wang ◽  
Xiaopan Dong
Keyword(s):  
Image Segmentation ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Affinity Propagation ◽  
Texture Image ◽  
Texture Image Segmentation ◽  
Two Phases ◽  
Representative Points

Clustering is a popular and effective method for image segmentation. However, existing cluster methods often suffer the following problems: (1) Need a huge space and a lot of computation when the input data are large. (2) Need to assign some parameters (e.g. number of clusters) in advance which will affect the clustering results greatly. To save the space and computation, reduce the sensitivity of the parameters, and improve the effectiveness and efficiency of the clustering algorithms, we construct a new clustering algorithm for image segmentation. The new algorithm consists of two phases: coarsening clustering and exact clustering. First, we use Affinity Propagation (AP) algorithm for coarsening. Specifically, in order to save the space and computational cost, we only compute the similarity between each point and its t nearest neighbors, and get a condensed similarity matrix (with only t columns, where t << N and N is the number of data points). Second, to further improve the efficiency and effectiveness of the proposed algorithm, the Self-tuning Spectral Clustering (SSC) is used to the resulted points (the representative points gotten in the first phase) to do the exact clustering. As a result, the proposed algorithm can quickly and precisely realize the clustering for texture image segmentation. The experimental results show that the proposed algorithm is more efficient than the compared algorithms FCM, K-means and SOM.

scholarly journals Segmentation of image based on k-means and modified subtractive clustering

2021 ◽  
Vol 22 (3) ◽  
pp. 1396
Author(s):  
Simon Tongbram ◽  
Benjamin A. Shimray ◽  
Loitongbam Surajkumar Singh
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Medical Science ◽  
Cluster Center ◽  
Subtractive Clustering ◽  
Cluster Centroid ◽  
Segmentation Methods ◽  
Segmentation Approach ◽  
Data Points

Image segmentation has widespread applications in medical science, for example, classification of different tissues, identification of tumors, estimation of tumor size, surgery planning, and atlas matching. Clustering is a widely implemented unsupervised technique used for image segmentation mainly because of its simplicity and fast computation. However, the quality and efficiency of clustering-based segmentation is highly depended on the initial value of the cluster centroid. In this paper, a new hybrid segmentation approach based on k-means clustering and modified subtractive clustering is proposed. K-means clustering is a very efficient and powerful algorithm but it requires initialization of cluster centroid. And, the consistency of the clustering outcomes of k-means algorithm depends on the initial selection of the cluster center. To overcome this drawback, a modified subtractive clustering algorithm based on distance relations between cluster centers and data points is proposed which finds a more accurate cluster centers compared to the conventional subtractive clustering. These cluster centroids obtained from the modified subtractive clustering are used in k-means algorithm for segmentation of the image. The proposed method is compared with other existing conventional segmentation methods by using several synthetic and real images and experimental finding validates the superiority of the proposed method.

scholarly journals Fast Medical Image Auto-segmentation for Bleeding Gastric Tissue Detection based on Deep DuS-KFCM Clustering

2021 ◽  
Author(s):  
Xian Xian Liu ◽  
Gloria Li ◽  
Wei Luo ◽  
Juntao Gao ◽  
Simon Fong
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Diagnostic System ◽  
Lesion Detection ◽  
Experimental Results ◽  
Gastric Bleeding ◽  
Bleeding Lesion ◽  
Adjacent Structures ◽  
Benchmark Datasets ◽  
Occurrence Matrix

Detection and classification of gastric bleeding tissues are one of the challenging tasks in endoscopy image analysis. Lesion detection plays an important role in gastric cancer (GC) diagnosis and follow-up. Manual segmentation of endoscopy images is a very time-consuming task and subject to intra- and inter-rater variability. Accurate GB segmentation in abdominal sequences is an essential and crucial task for surgical planning and navigation in gastric lesion ablation. However, GB segmentation in endoscope is a substantially challenging work because the intensity values of gastric blood are similar to those of adjacent structures. Objective: In this paper the idea is to combine two parts: Neural Network and Fuzzy Logic--Hybrid Neuro-Fuzzy system. The objective of this manuscript is to provide an efficient way to segment the gastric bleeding lesion area. This work focuses on design and development of an automated diagnostic system using gastric bleeding cancer endoscopy images. Methods: In this paper, a coarse-to-fine method was applied to segment gastric bleeding lesion from endoscopy images, which consists of two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm combined with spatial gray level co-occurrence matrix (GLCM) and the refined segmentation is implemented with deeplabv3+ (backbone with resnet50) algorithm to improve the overall accuracy. Results: Experimental results for gastric bleeding segmentation show that the method provides an accuracy of 87.9476% with specificity of 96.3343% and performs better than other related methods. Conclusions: The performance of the method was evaluated using two benchmark datasets: The GB Segmentation and the healthy datasets. Then use the gastric red spots (GRS) dataset to do the final test to verify weak bleeding symptoms. Our method achieves high accuracy in gastric bleeding lesion segmentation. The work describes an innovative way of using GLCM based textural features to extract underlying information in gastric bleeding cancer imagery. Modified deep DuS-KFCM endoscopy image segmentation method based on GLCM feature, The experimental results shown to be effective in image segmentation and has good performance of resisting noise, segmentation effect more ideal.

scholarly journals Robust Deep Kernel-Based Fuzzy Clustering With Spatial Information for Image Segmentation

2021 ◽  
Author(s):  
Lujia Lei ◽  
Chengmao Wu ◽  
Xiaoping Tian
Keyword(s):  
Image Segmentation ◽  
Kernel Function ◽  
Loss Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Noise Robustness ◽  
Neighborhood Information ◽  
Classical Image

Abstract Clustering algorithms with deep neural network have attracted wide attention of scholars. A deep fuzzy K-means clustering algorithm model with adaptive loss function and entropy regularization (DFKM) is proposed by combining automatic encoder and clustering algorithm. Although it introduces adaptive loss function and entropy regularization to improve the robustness of the model, its segmentation effect is not ideal for high noise; At the same time, its model does not use a convolutional auto-encoder, which is not suitable for high-dimensional images.Therefore, on the basis of DFKM, this paper focus on image segmentation, combine neighborhood median and mean information of current pixel, introduce neighborhood information of membership degree, and extend Euclidean distance to kernel space by using kernel function, propose a dual-neighborhood information constrained deep fuzzy clustering based on kernel function (KDFKMS). A large number of experimental results show that compared with DFKM and classical image segmentation algorithms, this algorithm has stronger anti-noise robustness.

Rough Entropy Clustering Algorithm in Image Segmentation

2010 ◽  
pp. 285-304
Author(s):  
Dariusz Malyszko ◽  
Jaroslaw Stepaniuk
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Rough Set ◽  
Rough Sets ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Entropy Measures ◽  
Segmentation Algorithms ◽  
Data Grouping ◽  
Rough Region

Clustering understood as a data grouping technique represents fundamental procedures in image processing. The present chapter’s concerns are combining the concept of rough sets and entropy measures in the area of image segmentation. In this context, comprehensive investigations into rough set entropy based clustering image segmentation techniques have been performed. Segmentation presents low-level image transformation routines concerned with image partitioning into distinct disjoint and homogenous regions. In the area of segmentation routines, threshold based algorithms and clustering algorithms most often are applied in practical solutions when there is a pressing need for simplicity and robustness. Rough entropy threshold based segmentation algorithms simultaneously combine optimal threshold determination with rough region approximations and region entropy measures. In the present chapter, new algorithmic schemes RECA in the area of rough entropy based partitioning routines have been proposed. Rough entropy clustering incorporates the notion of rough entropy into clustering models, taking advantage of dealing with some degree of uncertainty in analyzed data. RECA algorithmic schemes performed usually equally robust compared to standard k-means algorithms. At the same time, in many runs they yielded slightly better performances making possible future implementation in clustering applications.

scholarly journals Combination of Color and Focus Segmentation for Medical Images with Low Depth-of-Field

2018 ◽  
Vol 4 (1) ◽  
pp. 345-349
Author(s):  
Tamara Wirth ◽  
Ady Naber ◽  
Werner Nahm
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Medical Images ◽  
Image Data ◽  
Image Understanding ◽  
Surgical Instruments ◽  
Second Step ◽  
Depth Of Field ◽  
Segmentation Methods ◽  
Edge Based

AbstractImage segmentation plays an increasingly important role in image processing. It allows for various applications including the analysis of an image for automatic image understanding and the integration of complementary data. During vascular surgeries, the blood flow in the vessels has to be checked constantly, which could be facilitated by a segmentation of the affected vessels. The segmentation of medical images is still done manually, which depends on the surgeon’s experience and is time-consuming. As a result, there is a growing need for automatic image segmentation methods. We propose an unsupervised method to detect the regions of no interest (RONI) in intraoperative images with low depth-of-field (DOF). The proposed method is divided into three steps. First, a color segmentation using a clustering algorithm is performed. In a second step, we assume that the regions of interest (ROI) are in focus whereas the RONI are unfocused. This allows us to segment the image using an edge-based focus measure. Finally, we combine the focused edges with the color RONI to determine the final segmentation result. When tested on different intraoperative images of aneurysm clipping surgeries, the algorithm is able to segment most of the RONI not belonging to the pulsating vessel of interest. Surgical instruments like the metallic clips can also be excluded. Although the image data for the validation of the proposed method is limited to one intraoperative video, a proof of concept is demonstrated.

MRI SEGMENTATION BY FUZZY CLUSTERING METHOD BASED ON PRIOR KNOWLEDGE

2016 ◽  
Vol 28 (04) ◽  
pp. 1650025
Author(s):  
Mahsa Badiee Yazdi ◽  
Mohammad Mahdi Khalilzadeh ◽  
Mohsen Foroughipour
Keyword(s):  
Image Segmentation ◽  
White Matter ◽  
Prior Knowledge ◽  
Gray Matter ◽  
Clustering Algorithm ◽  
Similarity Index ◽  
Noise Levels ◽  
Fcm Algorithm ◽  
Segmentation Methods ◽  
Fuzzy Clustering Method

Image segmentation is often required as a fundamental stage in medical image processing, particularly during the clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) clustering algorithm is one of the best known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MRI images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentations done by experts, prior knowledge can be useful to modify image segmentation methods. In this paper, we propose some methods to modify FCM algorithm using expert manual segmentation as prior knowledge. We developed combination of FCM algorithm and prior knowledge in three ways, in order to improve segmentation of brain MR images with high noise level and spatial complexity. In real images, we had a considerable improvement in similarity index of three classes (white matter, gray matter, CSF) and in simulated images with different noise levels evaluation criteria of white matter and gray matter has improved.

Exploration of Morphological Procedure on the Recognition of Fundus Image

2020 ◽  
Vol 17 (5) ◽  
pp. 2014-2020
Author(s):  
S. Agnes Shifani ◽  
G. Ramkumar ◽  
V. Nanammal ◽  
R. Thandaiah Prabu
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Fundus Image ◽  
Science Field ◽  
Morphological Image Processing ◽  
Morphological Operator ◽  
Morphological Image ◽  
Mri Image

A gainful fuzzy k-means clustering algorithm under Morphological Image Processing (MIP) is performed. Image processing is one of quickly developing examination territory nowadays and now it is particularly coordinated with all identified with science field. Image Processing can be utilized for breaking down various restorative and MRI Image to get the uncommon and anomaly in the image. Image segmentation manages segmentation of vein segmentation algorithm utilizing fundus Image. In this task, this segmentation is done utilizing k-means clustering and c-means clustering algorithm and Morphological operator for better execution. This upgrades the vein variations from the norm progressively and in a moderately brief time when contrasted with numerous other clustering algorithms.

scholarly journals K-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 610 ◽  
Author(s):  
Senquan Yang ◽  
Pu Li ◽  
HaoXiang Wen ◽  
Yuan Xie ◽  
Zhaoshui He
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Color Image ◽  
Clustering Algorithms ◽  
Color Image Segmentation ◽  
Color Classification ◽  
Clustering Problem ◽  
Illumination Changes ◽  
Segmentation Approach

Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.

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