International Journal of Innovations and Implementations in Engineering IJIIE-ISSN 2454- 3489
Publication 2018 April
ATHIRA K H, ANEESH R P
Abstract - — Anomalous growth of cells or tissues in the liver will lead to liver cancer which has a low survival rate. Usually, the symptoms of the liver cancer do not appear until the cancer is in the severe stage. Recent studies show that all liver abnormalities lead to malignant unless it is properly treated. So, proper detection of liver abnormality in its initial stage is very necessary. Even though the needle biopsy is a good method to detect the liver cancer, its invasive nature catalyzes the mortality of the diseases. Therefore the biomedical imaging technique is being used as noninvasive imaging methods to analyze the liver abnormality. This paper presents an efficient machine learning algorithm to recognize the liver abnormality using texture analysis. Segmentation based Fractal Texture Analysis (SFTA) is used here to extract the texture features from the segmented Liver and Naïve Bayes classifier is used to achieve the automated classification. This algorithm is successfully tested with Atlas Liver images having the abnormalities like Cirrhosis, fatty, Hepatocellular Carcinoma (HCC), hemangioma, steatohepatitis, and liver tumour. .
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