scholarly journals Segmentasi Citra Berbasis Clustering Menggunakan Algoritma Fuzzy C-Means

2015 ◽  
Vol 14 (1) ◽  
Author(s):  
I Made Budi Adnyana ◽  
IKetut Gede Darmaputra ◽  
I Putu Agung Bayupati
Keyword(s):  
Image Segmentation ◽  
Objective Function ◽  
Execution Time ◽  
System Evaluation ◽  
Region Merging ◽  
Cluster Validity ◽  
Fuzzy C Means ◽  
Spatial Function ◽  
Statistical Region Merging ◽  
Fuzzy C Means Algorithm

Clustering based image segmentation in this study using Fuzzy C means algorithm with Xie Beni Index as an objective function. Preprocessing applied in this model using Statistical Region merging. Spatial function applied in Fuzzy C means method to reduce noise in clustering. The system evaluation is done by measuring cluster validity value (Xie Beni Index), execution time, and number of iteration. Experimental results on three test images illustrates the proposed method able to perform image segmentation well.

scholarly journals A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

2011 ◽  
Vol 07 (01) ◽  
pp. 155-171 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Segmentation ◽  
Point Of View ◽  
Neutrosophic Set ◽  
Convergence Criterion ◽  
Complex Objects ◽  
Fuzzy C Means ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

Wound Assessment in Pedobarography Using Image Segmentation Techniques

2021 ◽  
Vol 11 (5) ◽  
pp. 1403-1409
Author(s):  
D. Sudarvizhi ◽  
M. Akila
Keyword(s):  
Diabetes Mellitus ◽  
Image Segmentation ◽  
Diabetic Foot ◽  
Execution Time ◽  
Musculoskeletal Diseases ◽  
Foot Ulcer ◽  
Foot Ulcers ◽  
Primary Concern ◽  
Diabetic Ulcer ◽  
Fuzzy C Means

Pedobarography is elementary for kinetic gait analysis along with the analysis and exploration of multiple neurological and musculoskeletal diseases. One person among 11 adults suffer from Diabetes Mellitus. Also, Foot ulcers (FU) is a most harmful as well as associated chronic complications springing from diabetes mellitus (DM). Recently, there has been an evolving awareness that understanding the biomechanical factors beneath the diabetic ulcer in a better manner could result in improving the control activities over the disease, with considerable socio-economic effects. Diabetic Foot Ulcers (DFU) is a primary concern of this health issue, and if this is not addressed right can result in amputation. So in this research, the Image segmentation algorithms and Perimeter pixel comparison is carried out for wound classification depending on the simulation algorithm like Adaptive K-means, Clustering K means, Fuzzy C means, and Region growing approaches and among them, Fuzzy C means is found to achieve greatest accuracy of perimeter pixel values, which are 603, 462 and 356 pixel values in stages one, two and three. The time taken for execution among all the four simulation algorithms are observed and it can be revealed that Adaptive K means yields the least execution time for carrying out the simulation of foot ulcer. An evaluation on the self-assessment of wounds caused during diabetic foot ulcer employing image segmentation is developed. It is ultimately found that the objective of the image analysis pertaining to the ulcer in foot is the dynamic evaluation and definition of regions of high pressure in a diabetic patient’s foot depending on the estimations made on the perimeter pixel comparison and execution time.

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