Detection and Classification of Focal Liver Lesions using Support Vector Machine Classifiers
DOI:
https://doi.org/10.14738/jbemi.31.1821Keywords:
Ultrasound, Focal liver lesions, Feature extraction, Classification, Support vector machine classifierAbstract
In the present work, two computer aided diagnostic systems are designed to detect and classify focal liver lesions such as Cyst, Hemangioma, Hepatocellular carcinoma and Metastases. The work evaluates clinically acquired ultrasound image database. Database contains 111 liver images comprising 95 images of focal lesions and 16 images of normal liver. Images are enhanced and manually segmented into 800 non-overlapping segmented-regions-of-interest. Afterwards, 208 textual features are extracted from each segmented-regions-of-interest. First diagnostic system is designed with one-against-one multiclass support vector machine classification approach showing 93.1% (512/550) overall accuracy on test dataset. Second system is designed with tree structured approach using four binary support vector machine classifiers showing 86.9% (478/550) overall accuracy on test dataset. Out of these two, one-against-one approach based diagnostic system outperforms the neural network based diagnostic system designed for the same purpose by providing 96.6% classification accuracy for typical cases and 85.3% for atypical cases.
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