scholarly journals Image Segmentation Using PSO-Enhanced K-Means Clustering and Region Growing Algorithms

2021 ◽  
pp. 4988-4998
Author(s):  
Nassir H. Salman ◽  
Suhaila N. Mohammed
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Computational Cost ◽  
Region Growing ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Particle Swarm Intelligence ◽  
Discriminative Feature ◽  
Short Processing Time ◽  
Map Generation

    Image segmentation is a basic image processing technique that is primarily used for finding segments that form the entire image. These segments can be then utilized in discriminative feature extraction, image retrieval, and pattern recognition. Clustering and region growing techniques are the commonly used image segmentation methods. K-Means is a heavily used clustering technique due to its simplicity and low computational cost. However, K-Means results depend on the initial centres’ values which are selected randomly, which leads to inconsistency in the image segmentation results. In addition, the quality of the isolated regions depends on the homogeneity of the resulted segments. In this paper, an improved K-Means clustering algorithm is proposed for image segmentation. The presented method uses Particle Swarm Intelligence (PSO) for determining the initial centres based on Li’s method. These initial centroids are then fed to the K-Means algorithm to assign each pixel into the appropriate cluster. The segmented image is then given to a region growing algorithm for regions isolation and edge map generation. The experimental results show that the proposed method gives high quality segments in a short processing time.

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.

Review on 2D and 3D MRI Image Segmentation Techniques

2019 ◽  
Vol 15 (2) ◽  
pp. 150-160 ◽  
Author(s):  
S. Shirly ◽  
K. Ramesh
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Diagnostics ◽  
Processing Technique ◽  
Image Features ◽  
Medical Image Segmentation ◽  
Image Processing Technique ◽  
Edge Based ◽  
Mri Image

Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. </P><P> Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

scholarly journals Beef Quality Identification Using Thresholding Method and Decision Tree Classification Based on Android Smartphone

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kusworo Adi ◽  
Sri Pujiyanto ◽  
Oky Dwi Nurhayati ◽  
Adi Pamungkas
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Decision Tree ◽  
Processing Technique ◽  
Automated System ◽  
Image Processing Technique ◽  
Marbling Score ◽  
Beef Quality ◽  
Thresholding Method ◽  
Acquisition Processes

Beef is one of the animal food products that have high nutrition because it contains carbohydrates, proteins, fats, vitamins, and minerals. Therefore, the quality of beef should be maintained so that consumers get good beef quality. Determination of beef quality is commonly conducted visually by comparing the actual beef and reference pictures of each beef class. This process presents weaknesses, as it is subjective in nature and takes a considerable amount of time. Therefore, an automated system based on image processing that is capable of determining beef quality is required. This research aims to develop an image segmentation method by processing digital images. The system designed consists of image acquisition processes with varied distance, resolution, and angle. Image segmentation is done to separate the images of fat and meat using the Otsu thresholding method. Classification was carried out using the decision tree algorithm and the best accuracies were obtained at 90% for training and 84% for testing. Once developed, this system is then embedded into the android programming. Results show that the image processing technique is capable of proper marbling score identification.

scholarly journals Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Oludayo O. Olugbara ◽  
Emmanuel Adetiba ◽  
Stanley A. Oyewole
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Computational Cost ◽  
Computational Time ◽  
Limiting Factor ◽  
Pixel Intensity ◽  
Computational Costs ◽  
Segmentation Algorithms ◽  
Segmentation Problem ◽  
Significant Attention

Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering based algorithms were developed as multidimensional extensions of thresholding. While these algorithms have demonstrated successful results for fewer thresholds, their computational costs for a large number of thresholds are still a limiting factor. We propose a new clustering algorithm based on linear partitioning of the pixel intensity set and between-cluster variance criterion function for multilevel image segmentation. The results of testing the proposed algorithm on real images from Berkeley Segmentation Dataset and Benchmark show that the algorithm is comparable with state-of-the-art multilevel segmentation algorithms and consistently produces high quality results. The attractive properties of the algorithm are its simplicity, generalization to a large number of clusters, and computational cost effectiveness.

scholarly journals Estimation of Traffic Occupancy using Image Segmentation

2021 ◽  
Vol 11 (4) ◽  
pp. 7291-7295
Author(s):  
M. U. Farooq ◽  
A. Ahmed ◽  
S. M. Khan ◽  
M. B. Nawaz
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Road Traffic ◽  
Low Cost ◽  
Traffic Monitoring ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Percentage Difference ◽  
Novel Approach ◽  
Removal Technique

Increased traffic flow results in high road occupancy. Traffic road occupancy is often used as a parameter for the prediction of traffic conditions by traffic engineers. Although traffic monitoring systems are based on a large number of technologies, challenges are still present. Most of the methods work efficiently for free-flow traffic but not in heavy congestion. Image processing techniques are more effective than other methods, as they are based on loop sensors and detectors to monitor road traffic. A huge number of image frames are processed in image processing hence there is a need for a more efficient and low-cost image processing technique for accurate vehicle detection. In this paper, a novel approach is adopted to calculate road occupancy. The proposed framework has robust performance under road conjunction and diverse environmental conditions. A combination of image segmentation threshold technique and shadow removal technique is used. The study comprised of segmenting 1056 images extracted from recorded videos. The obtained results by image segmentation were compared with traffic road occupancy calculated manually using Autocad. A final percentage difference of 8.7 was observed.

Application of Digital Image Monitoring System in Strain Measurement of Cantilever Steel Beam

2010 ◽  
Vol 29-32 ◽  
pp. 112-117
Author(s):  
Peng Hui Li ◽  
Yin Ping Wen ◽  
Wen Guang Zhao ◽  
Hong Ping Zhu
Keyword(s):  
Digital Image ◽  
Strain Measurement ◽  
Region Growing ◽  
Calibration Method ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Steel Beam ◽  
Image Monitoring ◽  
Measurement Principle ◽  
Cubic Polynomial

The realization of strain measurement based on digital image processing technique is discussed. Combined with the strain measurement principle and the optical imaging feature, considered the axisymmetrical and non-axisymmetrical distortion, the automatic surface-fitting calibration method based on region growing is adopted. Cubic polynomial is applied to establish the relationship between the object space and pixel coordinate system. The expectations based on wavelet coefficients are used for sub-pixel edge detection, measurement accuracy can reach 0.02 pixels. Accuracy of displacement for strain measurement achieves micrometer-level. The theoretic validity and practical feasibility of the system are proved by the strain measurement of a cantilever steel beam.

IMAGE SEGMENTATION TECHNIQUES FOR RED BLOOD CELL : ON OVERVIEW

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
Laghouiter Oussama ◽  
M. Mahadi Abdul Jamil ◽  
Wan Mahani Hafiza Bt. Wan Mahmud
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Blood Cell ◽  
Blood Cells ◽  
Medical Image Analysis ◽  
White Blood Cells ◽  
Critical Factor ◽  
Processing Technique ◽  
Image Processing Technique ◽  
The Common

Image processing technique applies in different domains, such as medical, remote sensing and security. This techniques Aims to get a simple image called -image processed- should retain maximum useful information. The sensitive step in image processing is segmentation of image. Segmentation is first stage in medical image analysis seeded to two categories supervised and unsupervised technique. Accuracy of this stage affects the whole system performance. This paper present some methods applied for blood cell image segmentation and compares previous studies of overlapping cell division method. The common goal about this area is accuracy of counting the number of red blood cells (RBC) or white blood cells (WBC), which decrease with effect of some diseases such as anemia and leukemia. And makes it a critical factor in patient treatments.

A New Image Processing Technique for STEM

1974 ◽  
Vol 32 ◽  
pp. 432-433
Author(s):  
Yasushi Kokubo ◽  
Hirotami Koike ◽  
Teruo Someya
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Elastic Scattering ◽  
Field Emission ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Energy Analyzer ◽  
Scanning Microscope ◽  
Scanning Electron

One of the advantages of scanning electron microscopy is the capability for processing the image contrast, i.e., the image processing technique. Crewe et al were the first to apply this technique to a field emission scanning microscope and show images of individual atoms. They obtained a contrast which depended exclusively on the atomic numbers of specimen elements (Zcontrast), by displaying the images treated with the intensity ratio of elastically scattered to inelastically scattered electrons. The elastic scattering electrons were extracted by a solid detector and inelastic scattering electrons by an energy analyzer. We noted, however, that there is a possibility of the same contrast being obtained only by using an annular-type solid detector consisting of multiple concentric detector elements.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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