Liver tumor diagnosis by gray level and contourlet coefficients texture analysis

Kumar, S. S.; Moni, R. S.; Rajeesh, J.

doi:10.1109/icceet.2012.6203881

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…GA gives good results and converge to optimal solution, but has some problems such as pramature convergance, crossover, mutation and stuck in local minima. Kumar et al [25], applied PCA for feature selection and achieved total accuracy 88%. A 99.27% of correct classification and perfect agreement were obtained in our experiments with large dataset as seen in Table VI.…”

Section: A Discussionmentioning

confidence: 99%

“…Kumar et al [25], Improved his work by apply texture features using Gray-Level first-order statistics (GLFOS), Gray level co-occurrence matrix, Contour let coefficient first-order statistics (CCFOS), Contour let coefficient co-occurrence matrices (CCCMs) and for feature selection applied PCA. The classification accuracy based on PNN to classify liver tumor into HCC and Hemangioma.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Computational Intelligence Optimization Algorithm Based on Meta-heuristic Social-Spider: Case Study on CT Liver Tumor Diagnosis

ElSoud¹,

Anter²

2016

ijacsa

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Abstract-Feature selection is an importance step in classification phase and directly affects the classification performance. Feature selection algorithm explores the data to eliminate noisy, redundant, irrelevant data, and optimize the classification performance. This paper addresses a new subset feature selection performed by a new Social Spider Optimizer algorithm (SSOA) to find optimal regions of the complex search space through the interaction of individuals in the population. SSOA is a new natural meta-heuristic computation algorithm which mimics the behavior of cooperative social-spiders based on the biological laws of the cooperative colony. Different combinatorial set of feature extraction is obtained from different methods in order to keep and achieve optimal accuracy. Normalization function is applied to smooth features between [0,1] and decrease gap between features. SSOA based on feature selection and reduction compared with other methods over CT liver tumor dataset, the proposed approach proves better performance in both feature size reduction and classification accuracy. Improvements are observed consistently among 4 classification methods. A theoretical analysis that models the number of correctly classified data is proposed using Confusion Matrix, Precision, Recall, and Accuracy. The achieved accuracy is 99.27%, precision is 99.37%, and recall is 99.19%. The results show that, the mechanism of SSOA provides very good exploration, exploitation and local minima avoidance.

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Section: A Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Computational Intelligence Optimization Algorithm Based on Meta-heuristic Social-Spider: Case Study on CT Liver Tumor Diagnosis

ElSoud¹,

Anter²

2016

ijacsa

View full text Add to dashboard Cite

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“…PNN and CFBPN accuracy results are less when compared with BPN. Automatic lesion segmentation, texture feature extraction and classification of malignant and benign tumors is proposed [22]. Both first order statistic and second order statistic features are extracted from the gray level and contourlet detail coefficients.…”

Section: Neural Networkmentioning

confidence: 99%

A Study of Textural Analysis Methods for the Diagnosis of Liver Diseases from Abdominal Computed Tomography

Gunasundari¹,

Janakiraman²

2013

IJCA

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Liver diseases are considered seriously because liver is a vital organ to human beings. Computer aided liver analysis is a technique that can help radiologists to accurately identify diseases that can help in reducing the risk of liver surgery. The computer aided diagnosis (CAD) system consists of the segmentation of liver and lesion, extraction of features from a lesion and characterization of liver diseases by means of a classifier. In the last decade, the use of many segmentation techniques and classifier systems have been proposed by many authors with the intention to increase the performance of CAD systems This article focuses on various textural analysis methods used so far for the classification of liver diseases from abdominal Computed Tomography scans. It reviews the techniques and results of the various methods are analyzed and summarized. The future direction for the research is also discussed General TermsPattern Recognition, Survey

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“…When the number of classes is given as n, all points will be classified into n classes based on the membership degree and Euclidean distance between each point and class centre. In [29][30][31] the initial image was segmented by fuzzy c-means clustering and then smoothed by morphological processing. Then the candidate regions were analysed based on computing properties [29] or classified by neural network [30,31].…”

Section: Open Accessmentioning

confidence: 99%

“…In [29][30][31] the initial image was segmented by fuzzy c-means clustering and then smoothed by morphological processing. Then the candidate regions were analysed based on computing properties [29] or classified by neural network [30,31]. Finally, the regions which belong to liver or node were extracted.…”

Section: Open Accessmentioning

confidence: 99%

Review on the Methods of Automatic Liver Segmentation from Abdominal Images

Luo¹,

Li²,

Li³

2014

JCC

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Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy, automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In this paper, we present a new way of summarizing the latest achievements in automatic liver segmentation.We categorise a segmentation method according to the image feature it works on, therefore better summarising the performance of each category and leading to finding an optimal solution for a particular segmentation task. All the methods of liver segmentation are categorized into three main classes including gray level based method, structure based method and texture based method. In each class, the latest advance is reviewed with summary comments on the advantages and drawbacks of each discussed approach. Performance comparisons among the classes are given along with the remarks on the problems existed and possible solutions. In conclusion, we point out that liver segmentation is still an open issue and the tendency is that multiple methods will be employed together to achieve better segmentation performance.

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Liver tumor diagnosis by gray level and contourlet coefficients texture analysis

Cited by 18 publications

References 23 publications

Computational Intelligence Optimization Algorithm Based on Meta-heuristic Social-Spider: Case Study on CT Liver Tumor Diagnosis

Computational Intelligence Optimization Algorithm Based on Meta-heuristic Social-Spider: Case Study on CT Liver Tumor Diagnosis

A Study of Textural Analysis Methods for the Diagnosis of Liver Diseases from Abdominal Computed Tomography

Review on the Methods of Automatic Liver Segmentation from Abdominal Images

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