scholarly journals Segmentation of Thermogram Based on Region Based Technique using Split and Merge Method

2019 ◽  
Vol 8 (4) ◽  
pp. 9574-9578
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Object Representation ◽  
Region Of Interest ◽  
Region Growing ◽  
Destructive Testing ◽  
Segmentation Methods ◽  
Split And Merge ◽  
Thermographic Image ◽  
Non Destructive

The main aim of segmentation is to identify the Region of Interest for image analysis. The segregation of an image into meaningful structures is often an important phase in image analysis, object representation, visualization and also in various other image processing tasks. Image segmentation is mostly useful in applications like detection where it is difficult to process whole image at a time. In this paper Region based image segmentation is used to identify the delaminations in Thermographic image of Infrared Non-Destructive Testing. There are two basic techniques in Region based segmentation viz. Region growing method, splitting and merging method. New method based Split and Merge segmentation technique is employed to identify the defective regions in thermogram. Results obtained after segmentation as compared with state of art segmentation methods

A Survey on Solar Image Segmentation Techniques

2014 ◽  
Vol 945-949 ◽  
pp. 1899-1902
Author(s):  
Yuan Yuan Fan ◽  
Wei Jiang Li ◽  
Feng Wang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Analysis ◽  
Image Segmentation ◽  
Image Retrieval ◽  
Fundamental Issue ◽  
Solar Image ◽  
Segmentation Methods

Image segmentation is one of the basic problems of image processing, also is the first essential and fundamental issue in the solar image analysis and pattern recognition. This paper summarizes systematically on the image segmentation techniques in the solar image retrieval and the recent applications of image segmentation. Then the merits and demerits of each method are discussed in this paper, in this way we can combine some methods for image segmentation to reach the better effects in astronomy. Finally, according to the characteristics of the solar image itself, the more appropriate image segmentation methods are summed up, and some remarks on the prospects and development of image segmentation are presented.

Use of Image Analysis for Non-Destructive Testing of Thermoformed Food Packages

2021 ◽  
Vol 36 (5) ◽  
pp. 596-607
Author(s):  
O. Ekşi
Keyword(s):  
Image Analysis ◽  
Food Packaging ◽  
Thickness Distribution ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Food Packages ◽  
Food Package ◽  
Thickness Measurements ◽  
First Time ◽  
Non Destructive

Abstract The aim of this study is to determine the thickness distribution of a food package using a non-destructive method. Initially, thickness measurements were carried out using an experimental procedure for thermoformed samples that were used for food packaging. Additionally, in this study, image analysis was used for the first time to determine the thickness distribution of the thermoformed products non-destructively. Image analysis software was employed for the estimation of thickness distribution. Measured thickness results were compared to those estimated using image analysis. Based on the results of the current study, image analysis may be an alternative method for non-destructive testing of thermoformed food packages even in a mass production line. Image analysis can be used to determine not only thickness distribution but also the weakest regions in a food package.

Optimized Automatic Seeded Region Growing Algorithm with Application to ROI Extraction

2017 ◽  
Vol 17 (04) ◽  
pp. 1750024 ◽  
Author(s):  
Qianwen Li ◽  
Zhihua Wei ◽  
Cairong Zhao
Keyword(s):  
Image Analysis ◽  
Computational Complexity ◽  
Clustering Algorithm ◽  
Region Of Interest ◽  
Region Growing ◽  
Regions Of Interest ◽  
Automatic Extraction ◽  
Seeded Region Growing ◽  
Roi Extraction ◽  
Ap Clustering

Region of interest (ROI) is the most important part of an image that expresses the effective content of the image. Extracting regions of interest from images accurately and efficiently can reduce computational complexity and is essential for image analysis and understanding. In order to achieve the automatic extraction of regions of interest and obtain more accurate regions of interest, this paper proposes Optimized Automatic Seeded Region Growing (OASRG) algorithm. The algorithm uses the affinity propagation (AP) clustering algorithm to extract the seeds automatically, and optimizes the traditional region growing algorithm by regrowing strategy to obtain the regions of interest where target objects are contained. Experimental results show that our algorithm can automatically locate seeds and produce results as good as traditional region growing with seeds selected manually. Furthermore, the precision is improved and the extraction effect is better after the optimization with regrowing strategy.

scholarly journals Selected Applications of Scale Spaces in Microscopic Image Analysis

2015 ◽  
Vol 15 (7) ◽  
pp. 5-12
Author(s):  
Dimiter Prodanov ◽  
Tomasz Konopczynski ◽  
Maciej Trojnar
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Edge Detection ◽  
Scale Space ◽  
Microscopic Image ◽  
Space Theory ◽  
Systematic Treatment ◽  
Microscopic Image Analysis ◽  
Segmentation Methods ◽  
Scale Spaces

Abstract Image segmentation methods can be classified broadly into two classes: intensity-based and geometry-based. Edge detection is the base of many geometry-based segmentation approaches. Scale space theory represents a systematic treatment of the issues of spatially uncorrelated noise with its main application being the detection of edges, using multiple resolution scales, which can be used for subsequent segmentation, classification or encoding. The present paper will give an overview of some recent applications of scale spaces into problems of microscopic image analysis. Particular overviews will be given to Gaussian and alpha-scale spaces. Some applications in the analysis of biomedical images will be presented. The implementation of filters will be demonstrated.

scholarly journals Evaluating the performance of various segmentation techniques in industrial radiographs

2014 ◽  
Vol 14 (1) ◽  
pp. 161-171
Author(s):  
Mythili Thirugnanam ◽  
S. Margret Anouncia
Keyword(s):  
Image Processing ◽  
Region Of Interest ◽  
Non Destructive Testing ◽  
Gray Scale ◽  
Destructive Testing ◽  
Radiographic Images ◽  
Segmentation Algorithms ◽  
Industrial Inspection ◽  
Pros And Cons ◽  
Non Destructive

Abstract At present, image processing concepts are widely used in different fields, such as remote sensing, communication, medical imaging, forensics and industrial inspection. Image segmentation is one of the key processes in image processing key stages. Segmentation is a process of extracting various features of the image which can be merged or split to build the object of interest, on which image analysis and interpretation can be performed. Many researchers have proposed various segmentation algorithms to extract the region of interest from an image in various domains. Each segmentation algorithm has its own pros and cons based on the nature of the image and its quality. Especially, extracting a region of interest from a gray scale image is incredibly complex compared to colour images. This paper attempts to perform a study of various widely used segmentation techniques in gray scale images, mostly in industrial radiographic images that would help the process of defects detection in non-destructive testing.

scholarly journals A New Method for Colour Image Segmentation

2016 ◽  
Vol 11 (6) ◽  
pp. 860 ◽  
Author(s):  
Gustavo Schleyer ◽  
Gastón Lefranc ◽  
Claudio Cubillos ◽  
Ginno Millán ◽  
Román Osorio-Comparán
Keyword(s):  
Image Segmentation ◽  
Processing Time ◽  
Region Growing ◽  
The Other ◽  
New Method ◽  
Colour Image ◽  
Colour Image Segmentation ◽  
Split And Merge ◽  
Homogeneous Regions

This paper presents an unsupervised algorithm of colour image segmentation. This method combines the advantages of the approaches based on split and merge and region growing, and the use of the RGB and HSV colour representation models. The effectiveness of the proposed method has been verified by the implementation of the algorithm using three different testing images with homogeneous regions, spatially compact and continuous. It was observed that the proposed algorithm outperforms the other analysed techniques requiring shorter processing time when compared with the other methods.

scholarly journals An Introspective Performance Analysis of Threshold-based Segmentation Techniques on Digital Mammograms

YMER Digital ◽  
2021 ◽  
Vol 20 (11) ◽  
pp. 176-195
Author(s):  
A Nithya ◽  
◽  
P Shanmugavadivu ◽  
Keyword(s):  
Image Segmentation ◽  
Medical Image Analysis ◽  
Region Of Interest ◽  
Computation Time ◽  
Vital Role ◽  
Cancer Prognosis ◽  
Processing Step ◽  
Segmentation Methods ◽  
Reliable Source ◽  
Mammogram Images

Image segmentation, as a pre-processing step, plays a vital role in medical image analysis. The variants of threshold-based image segmentation methods are proved to offer feasible and optimal solutions to extract the region of interest (RoI), from medical images. Digital mammograms are used as a reliable source of breast cancer prognosis and diagnosis. Thresholding is a simple and effective strategy that finds applications in image processing and analysis. This research aimed to analyze the performance behaviour of a few threshold-based segmentation methods with respect to the intensity distribution of the input mammograms. For this analytical research, six automated thresholding segmentation techniques were chosen: Kapur, Otsu’s, Isoentropic, Ridler & Calvard’s, Kittler & Illingworth's, and Yen. The performance and behaviour of those methods were validated on the digital mammogram images of mini-MIAS database featured with Fatty (F), Fatty-Glandular (G), and Dense-Glandular (D) breast tissues. Those methods were analyzed on two metrics viz., Region Non-Uniformity (RNU) and computation time. The results of this research confirm that Ridler & Calvard’s method gives the best segmentation results for Dense-Glandular, Isoentropic method gives better segmentation results for Fatty and Yen method works well on the Fatty-Glandular normal mammogram images.

scholarly journals Segmenting radiographs of the hand and wrist using computer vision

1990 ◽  
Author(s):  
Γεώργιος Μάνος
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Bone Age ◽  
Growth Disorders ◽  
Region Merging ◽  
Clinical Tool ◽  
Age Assessment ◽  
Segmentation Methods ◽  
Bone Age Assessment ◽  
Edge Based

"Bone age" age assessment is an important clinical tool in the area of paediatrics. The technique is based upon the appearance and growth of specific bones in a developing child. In particular most methods for "bone age" assessment are based on the examination of the growth of bones of the left hand and wrist on X-ray films. This assessment is useful in the treatment of growth disorders and also is used to predict adult height. One of the most reliable methods for "bone age" assessment is the TW2 method. The drawback of this method is that it is time consuming and therefore its automation is highly desirable. One of the most important aspects of the automation process is image segmentation i.e. the extraction of bones from soft-tissue and background. Over the past 10 years various attempts have been made at the segmentation of handwrist radiographs but with limited success. This can mainly be attributed to the characteristics of the scenes e.g. biological objects, penetrating nature of radiation, faint bone boundaries, uncertainty of scene content, and conjugation of bones. Experience in the field of radiographic image analysis has shown thatsegmentation of radiographic scenes is a difficult task requiring solutions which depend on the nature of the particular problem.There are two main approaches to image segmentation: edge based and region based. Most of the previous attempts at the segmentation of hand-wrist radiographs were edge based. Edge based methods usually require a w-ell defined model of the object boundaries in order to produce successful results. However, for this particular application it is difficult to derive such a model. Region based segmentation methods have produced promising results for scenes which exhibit uncertainty regarding their content and boundaries of objects in the image, as in the case, for example, of natural senes. This thesis presents a segmentation method based on the concept of regions. This method consists of region growing and region merging stages. A technique was developed for region merging which combines edge and region boundai^ information. A bone extraction stage follows which labels regions as either boneor background using heuristic rules based on the grey-level properties of the scene. Finally, a technique is proposed for the segmentation of bone outlines which helps in identifying conjugated bones. Experimental results have demonstrated that this method represents a significant improvement over existing segmentation methods for hand-wrist radiographs, particularly with regard to the segmentation of radiographs with varying degrees of bone maturity.

scholarly journals Locally Optimal Subsampling Strategies for Full Matrix Capture Measurements in Pipe Inspection

2021 ◽  
Vol 11 (9) ◽  
pp. 4291
Author(s):  
Fabian Krieg ◽  
Jan Kirchhof ◽  
Eduardo Pérez ◽  
Thomas Schwender ◽  
Florian Römer ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Measurement Time ◽  
Pipe Inspection ◽  
Destructive Testing ◽  
Full Matrix ◽  
Array Configuration ◽  
Thresholding Algorithm ◽  
The Given ◽  
Non Destructive ◽  
Application Specific

In ultrasonic non-destructive testing, array and matrix transducers are being employed for applications that require in-field steerability or which benefit from a higher number of insonification angles. Having many transmit channels, on the other hand, increases the measurement time and renders the use of array transducers unfeasible for many applications. In the literature, methods for reducing the number of required channels compared to the full matrix capture scheme have been proposed. Conventionally, these are based on choosing the aperture that is as wide as possible. In this publication, we investigate a scenario from the field of pipe inspection, where cracks have to be detected in specific areas near the weld. Consequently, the width of the aperture has to be chosen according to the region of interest at hand. On the basis of ray-tracing simulations which incorporate a model of the transducer directivity and beam spread at the interface, we derive application specific measures of the energy distribution over the array configuration for given regions of interest. These are used to determine feasible subsampling schemes. For the given scenario, the validity/quality of the derived subsampling schemes are compared on the basis of reconstructions using the conventional total focusing method as well as sparsity driven-reconstructions using the Fast Iterative Shrinkage-Thresholding Algorithm. The results can be used to effectively improve the measurement time for the given application without notable loss in defect detectability.

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