Comparison and Evaluation of Methods for Liver Tumor Classification from CT Datasets

Gunasundari, S.; Ananthi, M.

doi:10.5120/5083-7333

Cited by 24 publications

(15 citation statements)

References 11 publications

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“…The number of features extracted varies based on positional difference values. The possible feature set extracted is 2, 5, 9, 14, 27, 44, 65 and 90, as (2,4), (4,2), (3,3), (0,7), (7,0), (6,1), (1,6), (2,5), (5,2), (3,4), (4,3), (0,8), (8,0), (1,7), (7,1), (2,6), (6,2), (3,5), (5,3), (4,4), (0,9), (9,0), (8,1), (1,8), (2,7), (7,2), (3,6), (6,3), (5,4), (4,5),(0,10), (10,0) , (1,9), (9,1), (2,8), …”

Section: Implementation and Resultsmentioning

confidence: 99%

“…Kumar et al (2010Kumar et al ( , 2011Kumar et al ( , and 2012 reveals that curvelet transforms [7], [8], and [9] achieve better than wavelet transform for categorization. Mala et al (2010) and Gunasundari et al (2012) concluded that the performance of PNN is good when it is compared with other neural networks [10]. A classifier [11] consisting of three sequentially placed neural networks for four classes of hepatic tissues was developed.…”

Section: Past Workmentioning

confidence: 99%

“…The liver is segmented from abdominal CT images using adaptive thresholding and morphological opening and closing (Gunasundari et al 2012). Based on medical knowledge obstacles are removed automatically.…”

Section: System Structurementioning

confidence: 99%

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Improved Feature Selection Based on Particle Swarm Optimization for Liver Disease Diagnosis

Gunasundari

Janakiraman

2013

Swarm, Evolutionary, and Memetic Computing

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Abstract. Dimensionality reduction of a feature set is a usual pre-processing step used for image classification to improve their accuracy. In this paper an automatic Computer Aided Diagnostic system (CAD) is proposed for detection of liver diseases like hepatoma and hemangioma from abdominal Computed Tomography (CT) images using an evolutionary approach for feature selection. The liver is segmented using adaptive thresholding. Histogram analyzer is used to fix the threshold and morphological operation is used for post processing. Rules are applied to remove the obstacles. Fuzzy c-Mean (FCM) clustering is used to extract the lesion from the segmented liver. Auto covariance features are extracted from the segmented lesion. The Binary Particle Swarm Optimization (BPSO) is applied to get the best reduced feature set. The textual information obtained after feature reduction was used to train Probabilistic Neural Network (PNN). The results obtained from different transfer functions are analyzed and compared.

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Section: Implementation and Resultsmentioning

confidence: 99%

Section: Past Workmentioning

confidence: 99%

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Improved Feature Selection Based on Particle Swarm Optimization for Liver Disease Diagnosis

Gunasundari

Janakiraman

2013

Swarm, Evolutionary, and Memetic Computing

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“…In feature extraction biorthogonal wavelet, Gray-level cooccurrence matrix and FDCT techniques are used [21]. The textual information obtained was used to train various neural networks such as BPN, PNN and Cascade feed forward BPN (CFBPN).…”

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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“…are used to train the SVM for classification. Mala et al [3] and Gunasundari and Anandhi [4] concluded that the performance of PNN is good when it is compared with other neural networks. Orthogonal moments [5] are used to classify the liver diseases from abdominal CT. Logistic maps and tent maps [6] are embedded in BPSO to find out the inertia weight of the BPSO.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid PSO-SFS-SBS Algorithm in Feature Selection for Liver Cancer Data

Gunasundari

Janakiraman

2014

Lecture Notes in Electrical Engineering

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Feature selection is an essential one in building high performance classification systems with the maximum classification accuracy. In this paper Particle Swarm Optimization (PSO) hybridized with Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS) algorithm is proposed for improving the performance of the classification system. The feature subsets are extracted from the pattern under classification using First Order Statistics (FOS) combined with the Co-occurrence based features for different distance and degrees. Binary Particle Swarm Optimization (BPSO) is applied to the feature subset. After some iteration the 30 % of the worst particles in PSO is replaced by the best feature subset of SFS and SBS algorithm. The proposed algorithm improves search ability and investigates two types of hybridization (1) PSO-SFS and (2) PSO-SFS-SBS with two options (1) velocity reset of all particles and (2) velocity reset of only worst particles. This hybrid system is applied to liver cancer data to reduce the features and to classify the liver disease as benign or malignant. Liver diseases like Hepatic Cellular Carcinoma (HCC), hemangioma, Focal Nodular Hyperplasia (FNH) and cholangiocarcinoma are classified. The Region of Interest (ROI) is cropped from an abdominal CT. The results obtained from different hybridized feature selection methods are examined. Experimental results show that the proposed methods select the 40 % of features as best features to train the Probabilistic Neural Network (PNN) classifier with insignificant time to categorize the disease to give the accuracy of 96.4 % for data set-1 and 92.6 % for data set-II.

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Comparison and Evaluation of Methods for Liver Tumor Classification from CT Datasets

Cited by 24 publications

References 11 publications

Improved Feature Selection Based on Particle Swarm Optimization for Liver Disease Diagnosis

Improved Feature Selection Based on Particle Swarm Optimization for Liver Disease Diagnosis

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

A Hybrid PSO-SFS-SBS Algorithm in Feature Selection for Liver Cancer Data

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