Detection and classification of liver cancers using computed tomography images

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Abstract Objectives The aim of this research was to develop an appropriate algorithm-routine that can automatically detect and classify liver cancers from the digital images of CT sample based on the texture and morphological features. Method The digital image processing (DIP) provides techniques first for liver image segmentation using semi-automatic technique from the enhanced abdominal CT images and classification using Artificial Neural Network (ANN) through texture features, then the liver tumor automatically extracted from the liver image using Otsu method which is a histogram based thresholding and the classification using ANN through morphological features. Result The samples were collected from a number of thirty six diagnosed patients in the procedure of abdominal CT images, at axial view in Black Lion Hospital from DICOM store in Addis Ababa. These images were selected to be processed and analyzed to get best generated result of the predefined classification model based on the extracted features. First nine texture features differentiate 12 normal and 24 abnormal liver images out of the given sample’s containing 36 images then tumor images extracted from the 24 abnormal images, the ANN classifier using six morphological features classified the 24 abnormal liver images as benign and malignant. It’s result presented by confusion matrix and showed that the maximum accuracy rate for tumor classification was 95.8%. The performance can be increased more by increasing the number of samples. Conclusions The outcome of this research work help radiologists to follow the condition of diseases at early stages and classify tumor as benign and malignant. It also helps in decreasing the rate of presence of disease by detecting cancer in its earlier stages.

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