Classification of Hepatic Lesions From CT Images Using Texture Features and Neural Networks

Gletsos, M.; Mougiakakou, Stavroula; Matsopoulos, G. K.; Nikita, K. S.; Kelekis, D.

doi:10.21236/ada412422

Cited by 9 publications

(8 citation statements)

References 4 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Classification of CT scan images using hepatic lesions were proposed by Glestos et al [9]. The input CT scan image of size 500 × 500 is taken as input.…”

Section: Proposed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Simple Computational Approach for the Texture Analysis of CT Scan Images Using Orthogonal Moments

Gomathinayagam¹,

Janakiraman²

2016

View full text Add to dashboard Cite

This paper is a study on texture analysis of Computer Tomography (CT) liver images using orthogonal moment features. Orthogonal moments are used as image feature representation in many applications like invariant pattern recognition of images. Orthogonal moments are proposed here for the diagnosis of any abnormalities on the CT images. The objective of the proposed work is to carry out the comparative study of the performance of orthogonal moments like Zernike, Racah and Legendre moments for the detection of abnormal tissue on CT liver images. The Region of Interest (ROI) based segmentation and watershed segmentation are applied to the input image and the features are extracted with the orthogonal moments and analyses are made with the combination of orthogonal moment with segmentation that provides better accuracy while detecting the tumor. This computational model is tested with many inputs and the performance of the orthogonal moments with segmentation for the texture analysis of CT scan images is computed and compared.

show abstract

“…Classification of CT scan images using hepatic lesions were proposed by Glestos et al [9]. The input CT scan image of size 500 × 500 is taken as input.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The idea so generated can be utilized this orthogonal moment for CT scan images. The classification of CT images using texture features and neural network was proposed by Glestos et al [9]. The Zernike polynomials are orthogonal to each other.…”

Section: Introductionmentioning

confidence: 99%

A Simple Computational Approach for the Texture Analysis of CT Scan Images Using Orthogonal Moments

Gomathinayagam¹,

Janakiraman²

2016

View full text Add to dashboard Cite

show abstract

“…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. Eight co-occurrence texture features are calculated for six different values of the pixel spacing.…”

Section: Past Workmentioning

confidence: 99%

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

Gunasundari

Janakiraman

2013

Swarm, Evolutionary, and Memetic Computing

View full text Add to dashboard Cite

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.

show abstract

“…A classifier consisting of three sequentially placed neural networks for four classes of hepatic tissues is developed [23]. 147 samples were used, 76 of which belong to healthy ones, 19 to cysts, 28 to hemangioma and 24 to HCC.…”

Section: Multi Level Neural Networkmentioning

confidence: 99%

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

Gunasundari¹,

Janakiraman²

2013

IJCA

View full text Add to dashboard Cite

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

show abstract

Classification of Hepatic Lesions From CT Images Using Texture Features and Neural Networks

Cited by 9 publications

References 4 publications

A Simple Computational Approach for the Texture Analysis of CT Scan Images Using Orthogonal Moments

A Simple Computational Approach for the Texture Analysis of CT Scan Images Using Orthogonal Moments

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

Contact Info

Product

Resources

About