Automated Focal Liver Lesion Staging Classification based on Haralick Texture Features and Multi-SVM

Abstract

This paper proposes automated identification and classification of various stages of focal liver lesions based on the Multi-Support Vector Machine (Multi-SVM). The proposed system can be used to discriminate focal liver diseases such as Cyst, Hemangioma, and Hepatocellular carcinoma along with normal liver. The multi-class scenario is a composition of a series of two-class problems, using oneagainst-all which is the earliest and one of the most widely used implementations. We formulate the discrimination between cysts, cavernous hemangioma, hepatocellular carcinoma, and normal tissue as a supervised learning problem, and apply Multi-SVM to classify the diseases using Haralick local texture descriptors and histogram based features calculated from Regions Of Interest (ROIs), as input. Selection of ROI significantly impact the classification performances, thus we proposes an automatic ROI selection using Fuzzy c-means initialized by level set technique. For multi-class classification, we adopt the One-Against-All (OAA) method. The proposed Multi-SVM based CAD system using 10-fold cross validation yielded classification accuracy of 96.11% with the individual class accuracy of 97.78%, 95.56%, 93.33% and 97.78% for NOR, Cyst, HEM and HCC cases respectively. The proposed Multi-SVM based system is compared with the K-Nearest Neighbor (KNN) based approaches. Experimental results have demonstrated that the Multi-SVM based system greatly outperforms KNN-based approaches and other methods in the literature. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of focal liver lesion diseases. General TermsMedical Image Processing, Image Features and Analysis, and Liver diagnosis.

Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network

“…Such methods can extract clinical features from each of the different focal liver lesions, and these features provide a more accurate diagnosis through a computerized classification method that can process a medical image with greater accuracy than through visual inspection [4][5][6].…”

Automated classification of dense calcium tissues in gray-scale intravascular ultrasound images using a deep belief network

“…Five features, namely mean, skewness, kurtosis, variance, and standard deviation were extracted from a 5 × 5 window mask. Detailed descriptions and equations are referred to in [36, 37].…”

Classification of Liver Diseases Based on Ultrasound Image Texture Features

“…A CAD system to distinguish between liver cancer and liver abscess has not been discussed. The main objective of this paper is to develop a reliable CAD system to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on the support vector machine (SVM) method [37][38][39][40][41] and ultrasound images of textural features. To date, there is no algorithm that is best in machine learning.…”