scholarly journals Efficient Way to Detect Bone Cancer using Image Segmentation

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1850-1854 ◽  
Keyword(s):  
Image Segmentation ◽  
Bone Disease ◽  
Bone Cancer ◽  
The Body ◽  
Malignant Growth ◽  
Fuzzy C Means ◽  
Testing Issue ◽  
Body Bone ◽  
The Way

Malignant growth is a wild division of irregular cells, which is spread over the parts of the body. Bone disease is one of the sorts of malignancy. Bone malignancy is a pernicious and threatening illness, caused because of uncontrolled division of cells in the bone. The most compromising and usually happened malignancy is bone disease. Prior the location of bone malignant growth is most testing issue. A definitive objective of this paper is to play out an examination on the bone disease pictures to discover the tumor. In this exploration we are looking at K-implies and fluffy C-Means grouping procedures to recognize the presize accuracy tumor part in the bone. In this exploration at first picture experiences into the division procedure and k-implies and Fuzzy C-Means calculations are connected to distinguish the exact tumor part in the bone. In this exploration is completely utilized MATLAB as a programming instrument for the way toward stacking a picture and to perform picture division. For clear comprehension of this exploration the outline and the outcomes will be shown in the sessions of this paper

scholarly journals IMAGE SEGMENTATION OF CHEST X-RAYS FOR ABNORMALITY PATTERN RECOGNATION IN LUNGS USING FUZZY C-MEANS METHOD

2018 ◽  
Vol 2 (2) ◽  
pp. 13-23
Author(s):  
Matheus Alvian Wikanargo ◽  
Angelina Pramana Thenata
Keyword(s):  
Image Segmentation ◽  
The Body ◽  
Normal Lung ◽  
X Rays ◽  
Processing Stage ◽  
X Ray ◽  
Fuzzy C Means ◽  
Segmented Image ◽  
Chest X Ray ◽  
Normal Lungs

The lungs are one of the important and vital organs in the body that function as a respiratory system process. One way to detect lung disease is to do an X-rays test. Chest X-ray is a radiographic projection to detect abnormalities in lung organ by using x-ray radiation. In the process of diagnosing, doctors see the condition of the results of Chest X-rays in the form of a thorax image (chest) to know the patient has an abnormal or normal lung. However, doctors' diagnosis of chest X-rays results-based abnormalities is likely to differ depending on the doctor's abilities and experience. This problem is expected to be solved by segmenting the lung image to help make the diagnosis appropriately. The purpose of this study is to conduct an analysis that can differentiate abnormal and normal lungs. The process of recognition of these patterns consists of the pre-processing stage of image segmentation by using morphology and then proceed to grouping by using fuzzy c-means method to express the pattern of the already segmented image. This research produces normal and abnormal lung images that can be identified with an accuracy of 80%.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

scholarly journals Mobilizing the Sense of “Fat”: A Phenomenological Materialist Approach

Human Studies ◽  
2021 ◽  
Author(s):  
Jenny Slatman
Keyword(s):  
Lived Experiences ◽  
Fat Body ◽  
The Body ◽  
New Materialism ◽  
Writing Style ◽  
Conceptual Frame ◽  
Fat Bodies ◽  
The Way ◽  
Phenomenological Exploration

AbstractThis paper aims to mobilize the way we think and write about fat bodies while drawing on Jean-Luc Nancy’s philosophy of the body. I introduce Nancy’s approach to the body as an addition to contemporary new materialism. His philosophy, so I argue, offers a form of materialism that allows for a phenomenological exploration of the body. As such, it can help us to understand the lived experiences of fat embodiment. Additionally, Nancy’s idea of the body in terms of a “corpus”—a collection of pieces without a unity—together with his idea of corpus-writing—fragmentary writing, without head and tail—can help us to mobilize fixed meanings of fat. To apply Nancy’s conceptual frame to a concrete manifestation of fat embodiment, I provide a reading of Roxane Gay’s memoir Hunger (2017). In my analysis, I identify how the materiality of fat engenders the meaning of embodiment, and how it shapes how a fat body can and cannot be a body. Moreover, I propose that Gay’s writing style—hesitating and circling – involves an example of corpus-writing. The corpus of corpulence that Gay has created gives voice to the precariousness of a fat body's materialization.

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.

Sign in / Sign up

Export Citation Format

Share Document