PEMILIHAN METODE SEGMENTASI PADA CITRA ULTRASONOGRAFI OVARIUM

Penelitian ini membandingkan metode segmentasi untuk mengenali folikel pada citra ultrasonografi ovarium, metode segmentasi yang paling baik akan digunakan untuk proses perhitungan jumlah folikel. Penilaian kinerja metode segmentasi active contour dan active contour without edge dievaluasi menggunakan Probabilistic Rand Index (PRI) dan Global Consistency Error (GCE). Hasil penelitian ini menunjukkan metode segmentasi yang terbaikadalah active contour without edge karena memiliki nilai PRI lebih tinggi dan pada nilai GCE lebih rendah dari pada hasil metode segmentasi active contour.

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Quantitative analysis for image segmentation by granular computing clustering from the view of set

Journal of Algorithms & Computational Technology ◽

10.1177/1748301819833050 ◽

2019 ◽

Vol 13 ◽

pp. 174830181983305 ◽

Cited By ~ 1

Author(s):

Hongbing Liu ◽

Xiaoyu Diao ◽

Huaping Guo

Keyword(s):

Image Segmentation ◽

Quantitative Assessment ◽

Granular Computing ◽

Clustering Algorithms ◽

Color Images ◽

Rand Index ◽

Global Consistency ◽

Different Shapes ◽

Partition Method ◽

Consistency Error

As partition method of set, granular computing clustering is applied to image segmentation evaluated by global consistency error, variation of Information, and Rand index from the view of set. Firstly, quantitative assessment of clustering is evaluated from the view of set. Secondly, granular computing clustering algorithms are induced by the distance formulas, the granules with different shapes are defined as the forms of vectors by different distance norms, especially, the atomic granule is induced by a point of space, the union operator realizes the transformation between two granule spaces and is used to form granular computing clustering algorithms. Thirdly, the image segmentations by granular computing clustering are evaluated from the view of set, such as global consistency error, variation of Information, and Rand index. Segmentations of the color images selected from BSD300 are used to show the superiority and feasibility for image segmentation by granular computing clustering compared with Kmeans and fuzzy c-means by experiments.

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Optimal Cellular Automata Technique for Image Segmentation

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8037.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1474-1478

Keyword(s):

Cellular Automata ◽

Search Algorithm ◽

Early Stage ◽

Gravitational Search Algorithm ◽

Cluster Center ◽

Cellular Automata Model ◽

Delay In Diagnosis ◽

Global Consistency ◽

Optimal Value ◽

Consistency Error

Leukemia death secured 10 thplace among the most dangerous death in the world. The main reason is due to the delay in diagnosis which in turn delayed the treatment process. Hence it becomes an exigent requirement to diagnose leukemia in its early stage. Segmentation of WBC is the initial phase of leukemia detection using image processing.This paper aims to extract WBC from the image background. There exists various techniques for WBC segmentation in the literature. Yet, they provides inaccurate results.Cellular Automata can be effectively implemented in image processing. In this paper, we have proposed an Optimal Cellular Automata approach for image segmentation.In this approach, the optimal value for alive cells is obtained through particle swarm Optimization with Gravitational Search Algorithm (PSOGSA). The optimal value have fed in to the cellular automata model and get the segmented image. The results are validated based on the parameters likeRand Index (RI), Global Consistency Error (GCE), and Variation of Information (VOI). The Experimental results of proposed technique shows better results when compared to the previously proposed techniques namely, Hybrid K-Means with Cluster Center Estimation, Region Splitting and Clustering Technique and Cellular Automata. The proposed technique outperformed all other techniques.

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Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

UHD Journal of Science and Technology ◽

10.21928/uhdjst.v4n1y2020.pp9-17 ◽

2020 ◽

Vol 4 (1) ◽

pp. 9 ◽

Cited By ~ 4

Author(s):

Zhana Fidakar Mohammed ◽

Alan Anwer Abdulla

Keyword(s):

Image Processing ◽

Information Hiding ◽

Blood Cells ◽

Medical Image ◽

White Blood Cells ◽

Segmentation Technique ◽

Research Areas ◽

Global Consistency ◽

Random Index ◽

Consistency Error

Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood images based on thresholding segmentation technique and it compares with the most commonly used segmentation technique which is known as color-k-means clustering. The comparison is done based on three well-known measurements, used for evaluating segmentation techniques which are probability random index, variance of information, and global consistency error. Experimental results demonstrate that the proposed thresholding-based segmentation technique provides better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of the proposed technique is less than the color-k-means which are 70.8144 ms and 204.7188 ms, respectively.

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Active Contour Driven by Local Gaussian Distribution Fitting and Local Signed Difference Based on Local Entropy

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416550119 ◽

2016 ◽

Vol 30 (03) ◽

pp. 1655011 ◽

Cited By ~ 1

Author(s):

Xiao Liang Jiang ◽

Bai Lin Li ◽

Jian Ying Yuan ◽

Xiao Liang Wu

Keyword(s):

Level Set ◽

Gaussian Distribution ◽

Active Contour ◽

Active Contour Model ◽

Local Entropy ◽

Distribution Fitting ◽

Proposed Model ◽

Global Consistency ◽

Weak Boundaries ◽

Level Set Formulation

Intensity inhomogeneity often causes considerable difficulties in image segmentation. In order to tackle this problem, we propose a novel region-based active contour model in a variational level set formulation. We first define a data fitting energy with a local Gaussian distribution fitting (LGDF) term, which induces a local force to attract the contour and stops it at object boundaries, and a local signed difference (LSD) term based on local entropy, which possesses both local separability and global consistency. This energy is then incorporated into a level set formulation with a level set regularization term that is necessary for accurate computation in the corresponding level set method. Experimental results show that the proposed model can not only segment images with intensity inhomogeneities and weak boundaries but also be robust to the noise, initial contours.

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Segmentation of Fetal Images using Kernel Fuzzy C Means Clustering with Whale Optimisation Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1482.0881119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 4122-4125

Keyword(s):

Predictive Value ◽

Performance Metrics ◽

Fuzzy C Means ◽

Optimisation Algorithm ◽

Segmentation Process ◽

Global Consistency ◽

Fuzzy C Means Clustering ◽

Improved Performance ◽

Critical Process ◽

Consistency Error

Image segmentation is considered as a critical process in medical imaging that are facilitated using automated computation. The segmentation process partitions the images into subsets based on its location or intensity. However, segmentation of fetal images faces poor segmentation due to the presence of noise and poor spatial intensities. In this paper, the study decomposes the fetal image into several parts for the purpose of segmentation and then performs the change in representations. The segmentation process is improved in this method using Kernel Fuzzy C Means (KFCM) based Whale Optimisation Algorithm (WOA). The segmentation process uses modified KFCM, where the centroid values are estimated using WOA. The segmentation method segments the input fetal image into appropriate regions using KFCM-WOA. The simulation result shows that the proposed method attains improved performance than other kernel based methods. The results of the performance metrics shows that the proposed method attains a sensitivity of 99.8273%, specificity of 99.7350%, accuracy of 99.9385%, positive predictive value (PPV) of 99.3964, Negative Predictive value (NPV) of 0.3805, Dice Coefficient of 48.5518, Rand Index (RI) of 0.9983 and Global consistency error (GCE) of 0.0460, which are higher than other kernel based methods.

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