Simple comparison of image segmentation algorithms based on evaluation criterion

Lukáč, Peter; Hudec, Róbert; Benčo, Miroslav; Kamencay, Patrik; Dubcová, Zuzana; Zachariášová, Martina

doi:10.1109/radioelek.2011.5936406

Cited by 18 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At all times, the operator of the calculator is I charge of determining the level of constrictions to utilize depending on the available amount of information. The EM calculation is a repeated numerical method to maximize the multipart possibility that pixel fits into the division [80]. The EM calculation involves two primary levels.…”

Section: ) Expectation Maximizationmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Muhammad

Zeebaree

Brifcani

et al. 2020

JASTT

View full text Add to dashboard Cite

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

show abstract

Section: ) Expectation Maximizationmentioning

confidence: 99%

“…The first level is guessing the possible allocation of the variable and, on the other level, is amplifying the loglikelihood operation. This repeated process iterates two stages and moves on until meeting at the local optimum [80].…”

Section: ) Expectation Maximizationmentioning

confidence: 99%

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Muhammad

Zeebaree

Brifcani

et al. 2020

JASTT

View full text Add to dashboard Cite

show abstract

“…The main drawback of K-means algorithm is the determination of K, number of cluster [24] and it cannot give the same result every time when algorithm is executed. Resulting cluster heavily depend on the initial assignment of centroids.…”

Section: Hard Clusteringmentioning

confidence: 99%

Automatic Detection of Abnormalities Associated with Abdomen and Liver Images: A Survey on Segmentation Methods

Chavan¹,

Rajput²

2016

IJCA

View full text Add to dashboard Cite

Image segmentation plays an important role in medical imaging by automating detection of false structures and other regions of interest. An image segmentation method partitions an image into multiple segments, representing an image into more meaningful, simpler and easier to analyze. Several general-purpose algorithm and techniques have been developed for image segmentation. This paper explains different segmentation techniques used in medical image analysis addressing the segmentation of abdominal and liver images as case study. Experiments are performed on abdominal and liver CT scan images and the outcomes of these segmentation techniques are discussed. Performance of the methods is presented on the basis of parameters namely, pixel values, mean and standard deviation.

show abstract

“…These three parameters determine the algorithm's efficiency by comparing boundaries of segments. The definition of precision P and recall R is given by [30,31]:…”

Section: Evaluation Criteriamentioning

confidence: 99%

An Advanced Approach to Extraction of Colour Texture Features Based on GLCM

Benčo

Hudec

Kamencay

et al. 2014

International Journal of Advanced Robotic Systems

Self Cite

View full text Add to dashboard Cite

This paper discusses research in the area of texture image classification. More specifically, the combination of texture and colour features is researched. The principle objective is to create a robust descriptor for the extraction of colour texture features. The principles of two well-known methods for grey-level texture feature extraction, namely GLCM (grey-level co-occurrence matrix) and Gabor filters, are used in experiments. For the texture classification, the support vector machine is used. In the first approach, the methods are applied in separate channels in the colour image. The experimental results show the huge growth of precision for colour texture retrieval by GLCM. Therefore, the GLCM is modified for extracting probability matrices directly from the colour image. The method for 13 directions neighbourhood system is proposed and formulas for probability matrices computation are presented. The proposed method is called CLCM (colour-level co-occurrence matrices) and experimental results show that it is a powerful method for colour texture classification.

show abstract

Simple comparison of image segmentation algorithms based on evaluation criterion

Cited by 18 publications

References 7 publications

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Automatic Detection of Abnormalities Associated with Abdomen and Liver Images: A Survey on Segmentation Methods

An Advanced Approach to Extraction of Colour Texture Features Based on GLCM

Contact Info

Product

Resources

About