Hybrid Segmentation Methods

2004 ◽  
pp. 351-388 ◽  
Author(s):  
Celina Imielinska ◽  
Yinpeng Jin ◽  
Elsa Angelini ◽  
Dimitris Metaxas ◽  
Ting Chen ◽  
...  
Keyword(s):  
Segmentation Methods ◽  
Hybrid Segmentation

scholarly journals Psycho-Behavioural Segmentation in Food and Nutrition: A Systematic Scoping Review of the Literature

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1795
Author(s):  
Eva L. Jenkins ◽  
Samara Legrand ◽  
Linda Brennan ◽  
Annika Molenaar ◽  
Mike Reid ◽  
...  
Keyword(s):  
Healthy Eating ◽  
Adult Population ◽  
Future Research ◽  
Dietary Intakes ◽  
Specific Population ◽  
Segmentation Methods ◽  
Food And Nutrition ◽  
Hybrid Segmentation ◽  
Behavioural Factors ◽  
Post Hoc

Inadequate dietary intakes are a key modifiable risk factor to reduce the risk of developing non-communicable diseases. To encourage healthy eating and behaviour change, innovative public health interventions are required. Social marketing, in particular segmentation, can be used to understand and target specific population groups. However, segmentation often uses demographic factors, ignoring the reasons behind why people behave the way they do. This review aims to explore the food and nutrition related research that has utilised psycho-behavioural segmentation. Six databases from were searched in June 2020. Inclusion criteria were: published 2010 onwards, segmentation by psycho-behavioural variables, outcome related to food or nutrition, and healthy adult population over 18 years. 30 studies were included; most were quantitative (n = 28) and all studies used post-hoc segmentation methods, with the tools used to segment the population varying. None of the segments generated were targeted in future research. Psycho-behavioural factors are key in understanding people’s behaviour. However, when used in post-hoc segmentation, do not allow for effective targeting as there is no prior understanding of behaviours that need to change within each segment. In future, we should move towards hybrid segmentation to assist with the design of interventions that target behaviours such as healthy eating.

scholarly journals USING A NEW HYBRID SEGMENTATION TECHNIQUE TO SEGMENT LUNAR CRATERS AND DETERMINE ITS DIAMETER

2021 ◽  
Vol 03 (02) ◽  
pp. 144-151
Author(s):  
Ahlam A. HUSSAIN ◽  
Ebtesam F. KANGER ◽  
Ban S ISMAEEL
Keyword(s):  
Hybrid Method ◽  
Accurate Determination ◽  
The Moon ◽  
Segmentation Method ◽  
Huge Number ◽  
Segmentation Methods ◽  
Lunar Craters ◽  
Hybrid Segmentation ◽  
Acceptable Outcome

Segmentation method is the process of partition digital image into parts depending to color, texture, and intensity. There are many segmentation methods used in different fields according to the purpose of application. In this study the global thresholding and proposed hybrid method were used to segment lunar craters. Craters on Moon's surface caused by collision between Moon and celestial objects as comet, meteorite, asteroids and others. Due to the Moon has no atmosphere, the lunar surface covered by a huge number of craters different in their size and depth depending on velocity and size of collided objects. The study of lunar craters provide information about the age and geology of a Moon's surface. So, we proposed a novel hybrid segmentation method to segment Moon's images and isolate lunar craters from other surface features and then determine the diameter of lunar craters. The proposed hybrid method combine the performance of K-Means and SFFNN together. The results shown that, the proposed method gives very acceptable outcome, where the boundaries of lunar craters were delineate in professional way that lead to accurate determination of its diameters.

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Deformable Models for Segmentation Based on Local Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jimena Olveres ◽  
Erik Carbajal-Degante ◽  
Boris Escalante-Ramírez ◽  
Enrique Vallejo ◽  
Carla María García-Moreno
Keyword(s):  
Local Binary Patterns ◽  
Distance Measures ◽  
Local Analysis ◽  
Hermite Transform ◽  
Active Shape Models ◽  
Shape Models ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Image Modality ◽  
New Algorithms

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

Microstructural and mechanical evaluations of region segmentation methods in classifications of osteonecrosis

2020 ◽  
pp. 110208
Author(s):  
Chenglong Feng ◽  
Lizhen Wang ◽  
Peng Xu ◽  
Zhaowei Chu ◽  
Jie Yao ◽  
...  
Keyword(s):  
Region Segmentation ◽  
Segmentation Methods

Revisiting the K-nn algorithm for obstetric image segmentation

2021 ◽  
pp. 1-13
Author(s):  
P. Salgado ◽  
T.-P. Azevedo Perdicoúlis
Keyword(s):  
Magnetic Resonance Image ◽  
Medical Image ◽  
Clustering Algorithm ◽  
Ultrasound Image ◽  
Well Being ◽  
Health Conditions ◽  
Medical Applications ◽  
Training Set ◽  
Segmentation Methods ◽  
Segmentation Algorithms

Medical image techniques are used to examine and determine the well-being of the foetus during pregnancy. Digital image processing (DIP) is essential to extract valuable information embedded in most biomedical signals. After, intelligent segmentation methods, based on classifier algorithms, must be applied to identify structures and relevant features from previous data. The success of both is essential for helping doctors to identify adverse health conditions from the medical images. To obtain easy and reliable DIP methods for foetus images in real-time, at different gestational ages, aware pre-processing needs to be applied to the images. Thence, some data features are extracted that are meant to be used as input to the segmentation algorithms presented in this work. Due to the high dimension of the problems in question, assemblage of the data is also desired. The segmentation of the images is done by revisiting the K-nn algorithm that is a conventional nonparametric classifier. Besides its simplicity, its power to accomplish high classification results in medical applications has been demonstrated. In this work two versions of this algorithm are presented (i) an enhancement of the standard version by aggregating the data apriori and (ii) an iterative version of the same method where the training set (TS) is not static. The procedure is demonstrated in two experiments, where two images of different technologies were selected: a magnetic resonance image and an ultrasound image, respectively. The results were assessed by comparison with the K-means clustering algorithm, a well-known and robust method for this type of task. Both described versions showed results close to 100% matching with the ones obtained by the validation method, although the iterative version displays much higher reliability in the classification.

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