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Fatima Hussain
Mehrun Nisa
Marina Riasat
Wajeha Shafqat
Nimra Batool
Faheem Afzal
Aalia Nazir
Ambreen Kalsoom


Texture analysis, CT images, MaZda, PCA, LDA, NDA, POE+ACC


Background: The incident of liver diseases among patients has been consistently increasing. Early detection and precise differentiation between benign and metastasis liver tumours could potentially lead to enhance the success rate. Computed tomography imaging serves as a practical medical imaging method for evaluating liver tumours.

Objective: The primary aim of this study is to classify liver CT images using texture features, with the goal of assessing the effectiveness of a computer-aided diagnostic system for detection of liver diseases.

Methods: The dataset used in this study comprises 162 cases of benign liver conditions and 128 cases of metastatic liver conditions. Statistical texture analysis techniques, such as Gray level run length matrix, and Co-occurrence matrix are employed to extract the texture features parameters from each region of interest. A normalization approach of μ ± 3σ is applied during this process to analyze the results, the Artificial Neural Network (ANN) classification method is utilized on Principal Component Analysis (PCA), Linear Discrimination Analysis (LDA) and Non- Linear Discrimination Analysis (NDA) techniques.

Results: The images are subjected to analysis using the analysis option available in MaZda software. By applying statistical features such as run length and gray level histogram, with normalization μ ± 3σ, the best results are obtained. This approach highlights the distinct difference between benign and metastasis liver diseases

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