Application of artificial neural networks for the classification of liver lesions by image texture parameters

Sujana, H.; Swarnamani, S.; Suresh, S.

doi:10.1016/s0301-5629(96)00144-5

Cited by 106 publications

(66 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Mittal et al [15] used an ROI size of 25×25 pixels while Sujana et al [28] and Poonguzhali et al [29] used an ROI size of 10×10 pixels for computing texture features. Virmani et al [16,21,22] used an ROI size of 32×32 pixels.…”

Section: Selection Of Roi Sizementioning

confidence: 99%

“…The study in [28] used first-order statistics (FOS) and gray level run length matrix (GLRLM) features for classification between NOR, HEM and malignant liver lesions by using linear discriminant analysis and neural network (NN) classifier. The study in [29] used gray level co-occurrence matrix (GLCM), autocorrelation, Laws' and edge frequency based texture features for classification of NOR, Cyst, HEM and malignant liver lesions by using a NN classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Their study reports maximum correct classification rate of 70 % by the use of GLCM and Laws' features. In both these researches [28,29] malignant lesions are considered as a single class; however, the diagnosis of malignant lesion as HCC or MET is clinically significant for effective treatment and management of liver malignancies [6,9,16]. In a recent study [16], GA-SVM based CAD system based on statistical, spectral and spatial filtering based texture features is proposed for binary classification between HCC and MET lesions.…”

Section: Introductionmentioning

confidence: 99%

“…In the present work, in order to design an efficient CAD system based on ensemble of NN classifiers, feature dimensionality reduction is carried out by using principal component analysis (PCA) for designing each classifier. In several studies [15,23,28,29], only ROIs within the lesions have been considered for computing texture features. Virmani et al [16,21,22] used both texture and texture ratio features for classification of FLLs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

et al. 2014

View full text Add to dashboard Cite

A neural network ensemble (NNE) based computeraided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.

show abstract

Section: Selection Of Roi Sizementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

et al. 2014

View full text Add to dashboard Cite

show abstract

“…The textural feature information is highly sensitive according to the number of pixels i.e. size of ROI [13]. At least 800 pixels are necessary in a selected ROI to obtain the reliable result of texture analysis [14].…”

Section: Background Theorymentioning

confidence: 99%

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

Tiwari¹,

Sachdeva²,

Ahuja³

et al. 2017

IJCIS

View full text Add to dashboard Cite

The iso, hypo or hyper intensity, similarity of shape, size and location complicates the identification of brain tumors. Therefore, an adequate Computer Aided Diagnosis (CAD) system is designed for classification of brain tumor for assisting inexperience radiologists in diagnosis process. A multifarious database of real post contrast T1-weighted MR images from 10 patients has been taken. This database consists of primary brain tumors namely Meningioma (MENI-class 1), Astrocytoma (AST-class 2), and Normal brain regions (NORM-class 3). The region of interest(s) (ROIs) of size 20 x 20 is extracted by the radiologists from each image in the database. A total of 371 texture and intensity features are extracted from these ROI(s). An Artificial Neural Network (ANN) is used to classify these three classes as it shows better classification results on multivariate non-linear, complicated, rule based domains, and decision making domains. It is being observed that ANN provides much accurate results in terms of individual classification accuracy and overall classification accuracy. The four discrete experiments have been performed. Initially, the experiment was performed by extracting 263 features and an overall classification accuracy 78.10% is achieved, however, it was noticed that MENI (class-1) was highly misclassified with AST (class-2). Further, to improve the overall classification accuracy and individual classification accuracy specifically for MENI (class-1), LAWs textural energy measures (LTEM) are added in the feature bank (263+108=371). An individual class accuracy of 91.40% is obtained for MENI (class-1), 91.43% for AST (class-2), 94.29% for NORM (class-3) and an overall classification accuracy of 92.43% is achieved. The results are calculated with and without addition of LTEM feature with Principle component analysis (PCA)-ANN. LTEM-PCA-ANN approach improved results with an overall accuracy of 93.34%. The texture patterns obtained were clear enough to differentiate between MENI (class-1) and AST (class-2) despite of necrotic and cystic component and location and size of tumor. LTEM detected fundamental texture properties such as level, edge, spot, wave and ripple in both horizontal and vertical directions which boosted the texture energy.

show abstract

Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

Perez‐Moreno

Domínguez

Gratacós

et al. 2018

J of Ultrasound Medicine

View full text Add to dashboard Cite

Objectives To compare the robustness of several methods based on quantitative ultrasound (US) texture analysis to evaluate its feasibility for extracting features from US images to use as a clinical diagnostic tool. Methods We compared, ranked, and validated the robustness of 5 texture‐based methods for extracting textural features from US images acquired under different conditions. For comparison and ranking purposes, we used 13,171 non‐US images from widely known available databases (OUTEX [University of Oulu, Oulu, Finland] and PHOTEX [Texture Lab, Heriot‐Watt University, Edinburgh, Scotland]), which were specifically acquired under different controlled parameters (illumination, resolution, and rotation) from 103 textures. The robustness of those methods with better results from the non‐US images was validated by using 666 fetal lung US images acquired from singleton pregnancies. In this study, 2 similarity measurements (correlation and Chebyshev distances) were used to evaluate the repeatability of the features extracted from the same tissue images. Results Three of the 5 methods (gray‐level co‐occurrence matrix, local binary patterns, and rotation‐invariant local phase quantization) had favorably robust performance when using the non‐US database. In fact, these methods showed similarity values close to 0 for the acquisition variations and delineations. Results from the US database confirmed robustness for all of the evaluated methods (gray‐level co‐occurrence matrix, local binary patterns, and rotation‐invariant local phase quantization) when comparing the same texture obtained from different regions of the image (proximal/distal lungs and US machine brand stratification). Conclusions Our results confirmed that texture analysis can be robust (high similarity for different condition acquisitions) with potential to be included as a clinical tool.

show abstract

Application of artificial neural networks for the classification of liver lesions by image texture parameters

Cited by 106 publications

References 15 publications

Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

Contact Info

Product

Resources

About