Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images

Kadah, Yasser M.; Farag, A.B.; Żurada, Jacek M.; Badawi, Ahmed M.; Youssef, A.-B.M.

doi:10.1109/42.511750

Cited by 236 publications

(143 citation statements)

References 22 publications

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“…This result is in accordance with [3,12]. The classification technique significantly affects the final diagnosis [10]. Using the LOOCV method, the same data set was tested with different types of classifiers, namely a kNN, a Bayes classifier and a SVM classifier with polynomial (SVM P ) and radial-basis (SVM R ) kernels.…”

Section: Original Us Imagementioning

confidence: 84%

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Diffuse Liver Disease Classification from Ultrasound Surface Characterization, Clinical and Laboratorial Data

Ribeiro

Marinho

Velosa

et al. 2011

Pattern Recognition and Image Analysis

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Abstract. In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

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Section: Original Us Imagementioning

confidence: 84%

“…The non-parametric kNN classifier is also tested in this paper. It classifies a test sample to a class according to the majority of the training neighbors in the feature space by using the minimum Euclidean distance criterion [10]. All classifiers were implemented using the algorithm proposed by [11].…”

Section: Problem Formulationmentioning

confidence: 99%

Diffuse Liver Disease Classification from Ultrasound Surface Characterization, Clinical and Laboratorial Data

Ribeiro

Marinho

Velosa

et al. 2011

Pattern Recognition and Image Analysis

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show abstract

“…Virmani et al [16,21,22] used an ROI size of 32×32 pixels. It is worth mentioning that the use of 10×10 pixels and even 25×25 pixels as ROI size yields a smaller number of pixels in comparison to the minimum 800 pixels required to estimate reliable statistics [16,18,[40][41][42]. Yoshida et al [23] used 64×64 pixels as ROI size, possibly because they used high-resolution scanned images instead of real US images.…”

Section: Selection Of Roi Sizementioning

confidence: 99%

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

et al. 2014

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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.

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“…The ROI size also depends on the application for which it will be used. Either too large ROI like 40×40 or too small ROI like 10×10 does contain too much or too less information for computation respectively [14]. An average size of brain tumor is 30×30.…”

Section: Segregating Regions Of Interest (Rois) From Databasementioning

confidence: 99%

“…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

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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.

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Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images

Cited by 236 publications

References 22 publications

Diffuse Liver Disease Classification from Ultrasound Surface Characterization, Clinical and Laboratorial Data

Diffuse Liver Disease Classification from Ultrasound Surface Characterization, Clinical and Laboratorial Data

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

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