scholarly journals Intelligent Automatic Segmentation of Wrist Ganglion Cysts Using DBSCAN and Fuzzy C-Means

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2329
Author(s):  
Kwang Baek Kim ◽  
Doo Heon Song ◽  
Hyun Jun Park
Keyword(s):  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Ganglion Cyst ◽  
High Reliability ◽  
False Positive Rate ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Ganglion Cysts

Ganglion cysts are common soft tissue masses of the hand and wrist, and small size cysts are often hypoechoic. Thus, identifying them from ultrasonography is not an easy problem. In this paper, we propose an automatic segmentation method using two artificial intelligence algorithms in sequence. A density based unsupervised learning algorithm called DBSCAN is performed as a front-end and its result determines the number of clusters used in the Fuzzy C-Means (FCM) clustering algorithm for quantification of ganglion cyst object. In an experiment using 120 images, the proposed method shows a higher extraction rate (89.2%) and lower false positive rate compared with FCM when the ground truth is set as the human expert’s decision. Such human-like behavior is more apparent when the size of the ganglion cyst is small that the quality of ultrasonography is often not very high. With this fully automatic segmentation method, the operator subjectivity that is highly dependent on the experience of the ultrasound examiner can be mitigated with high reliability.

Research on the Segmentation Method of Rice Leaf Disease Image

2012 ◽  
Vol 220-223 ◽  
pp. 1339-1344 ◽  
Author(s):  
Li Bo Liu
Keyword(s):  
Clustering Algorithm ◽  
Segmentation Method ◽  
Threshold Method ◽  
Fuzzy C Means ◽  
Rice Leaf ◽  
Leaf Disease ◽  
Otsu Method ◽  
Rice Disease ◽  
Fuzzy C Means Clustering ◽  
Iterative Threshold

In Order to Improve the Segmentation Effect of the Rice Leaf Disease Images, we Take Optimal Iterative Threshold Method,OTSU Method and Fuzzy C-means Clustering Algorithm to Make Adaptive Segmentation of Rice Disease Images which Were Collected under Different Circumstances. through Comparative Analysis, Experimental Results Show that: Three Methods All Can Effective Separate Spot from the Leaves; in Comparison, the Effect of the Fuzzy C-means Clustering Algorithm Is the Best, but the Number of Iterations Is too many and the Time Spent on it Is the Most; the Effect of OTSU Method Is Lesser, Optimal Iterative Threshold Method Is the Worst. Comprehensive Considering the Segmentation Accuracy and Efficiency, the Paper Chooses OTSU as the Segmentation Method of the Rice Leaf Disease Images.

scholarly journals A colour-based image segmentation method for the measurement of masticatory performance in older adults

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 191
Author(s):  
Lorenzo Scalise ◽  
Rachele Napolitano ◽  
Lorenzo Verdenelli ◽  
Susanna Spinsante ◽  
Giorgio Rappelli
Keyword(s):  
Older Adults ◽  
Clustering Algorithm ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Masticatory Performance ◽  
Robust Solution ◽  
Masticatory Efficiency ◽  
Time Period ◽  
Oral Conditions

Masticatory efficiency in older adults is an important parameter for the assessment of their oral health and quality of life. This study presents a measurement method based on the automatic segmentation of two-coloured chewing gum based on a <em>K</em>-means clustering algorithm. The solution proposed aims to quantify the mixed areas of colour in order to evaluate masticatory performance in different dental conditions. The samples were provided by ‘two-colour mixing’ tests, currently the most used technique for the evaluation of masticatory efficacy, because of its simplicity, low acquisition times and reduced cost. The image analysis results demonstrated a high discriminative power, providing results in an automatic manner and reducing errors caused by manual segmentation. This approach thus provides a feasible and robust solution for the segmentation of chewed samples. Validation was carried out by means of a reference software, demonstrating a good correlation (<em>R</em><sup>2 </sup>= 0.64) and the higher sensitivity of the proposed method (+75 %). Tests on patients with different oral conditions demonstrated that the <em>K</em>-means segmentation method enabled the automatic classification of patients with different masticatory conditions, providing results in a shorter time period (20 chewing cycles instead of 50).

Improvement of the Image Sub-Segmentation for Identification and Differentiation of Atypical Regions

2017 ◽  
Vol 32 (01) ◽  
pp. 1860011 ◽  
Author(s):  
B. Ojeda-Magaña ◽  
J. Quintanilla-Domínguez ◽  
R. Ruelas ◽  
L. Gómez Barba ◽  
D. Andina
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Digital Images ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Representative Element ◽  
First Case ◽  
Segmented Object ◽  
Segmentation Problem ◽  
Black Color

A new sub-segmentation method has been proposed in 2009 which, in digital images, help us to identify the typical pixels, as well as the less representative pixels or atypical of each segmented region. This method is based on the Possibilistic Fuzzy c-Means (PFCM) clustering algorithm, as it integrates absolute and relative memberships. Now, the segmentation problem is related to isolate each one of the objects present in an image. However, and considering only one segmented object or region represented by gray levels as its only feature, the totality of pixels is divided in two basic groups, the group of pixels representing the object, and the others that do not represent it. In the former group, there is a sub-group of pixels near the most representative element of the object, the prototype, and identified here as the typical pixels, and a sub-group corresponding to the less representative pixels of the object, which are the atypical pixels, and generally located at the borders of the pixels representing the object. Besides, the sub-group of atypical pixels presents greater tones (brighter or towards the white color) or smaller tones (darker or towards black color). So, the sub-segmentation method offers the capability to identify the sub-region of atypical pixels, although without performing a differentiation between the brighter and the darker ones. Hence, the proposal of this work contributes to the problem of image segmentation with the improvement on the detection of the atypical sub-regions, and clearly recognizing between both kind of atypical pixels, because in many cases only the brighter or the darker atypical pixels are the ones that represent the object of interest in an image, depending on the problem to be solved. In this study, two real cases are used to show the contribution of this proposal; the first case serves to demonstrate the pores detection in soil images represented by the darker atypical pixels, and the second one to demonstrate the detection of microcalcifications in mammograms, represented in this case by the brighter atypical pixels.

scholarly journals Efficient Fuzzy Image Stretching for Automatic Ganglion Cyst Extraction Using Fuzzy C-Means Quantization

2021 ◽  
Vol 11 (24) ◽  
pp. 12094
Author(s):  
Sun Joo Lee ◽  
Doo Heon Song ◽  
Kwang Baek Kim ◽  
Hyun Jun Park
Keyword(s):  
Image Enhancement ◽  
Ganglion Cyst ◽  
Treatment Phase ◽  
Benign Tumors ◽  
Appendicular Skeleton ◽  
Human Intervention ◽  
Fuzzy C Means ◽  
Ganglion Cysts ◽  
Fuzzy Image

Ganglion cysts are commonly observed in association with the joints and tendons of the appendicular skeleton. Ultrasonography is the favored modality used to manage such benign tumors, but it may suffer from operator subjectivity. In the treatment phase, ultrasonography also provides guidance for aspiration and injection, and the information regarding the accurate location of the pedicle of the ganglion. Thus, in this paper, we propose an automatic ganglion cyst extracting method based on fuzzy stretching and fuzzy C-means quantization. The proposed method, with its carefully designed image-enhancement policy, successfully detects ganglion cysts in 86 out of 90 cases (95.6%) without requiring human intervention.

scholarly journals Citizen science, cells and CNNs – deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

2020 ◽  
Author(s):  
Helen Spiers ◽  
Harry Songhurst ◽  
Luke Nightingale ◽  
Joost de Folter ◽  
Roger Hutchings ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Nuclear Envelope ◽  
Citizen Science ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Learning Approaches ◽  
High Quality ◽  
Ground Truth Data

AbstractAdvancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.

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