Automatic liver tumor segmentation from CT images using random forest algorithm

Abstract

Automatic liver segmentation is challenging, and the tumor segmenting process adds more complexity. Based on the grey levels and shape, separating the liver and tumor from abdominal CT images is critical. In our paper suggests a more effective approach by using Gabor features (GF) to segment liver tumors from CT images and three alternative neural network algorithms to address these problems: RF, CNN and ANN. This thesis uses the same collection of classifiers and GF to first segment a variety of Gabor liver images. The organ (liver) is then extracted from an abdominal CT image using liver segmentation, which is done by three classifiers: ANN, CNN, RF trained on Gabor filter and the tumor segmentation is done by the human visual system (HVS). For pixel-wise segmentation, reliable and accurate ML techniques were used. For the liver segmentation, the classification accuracy was 99.55, 97.88 and 98.13% for RF, CNN and ANN, respectively. From the extracted image of liver, the classification accuracy for tumor was 99.52, 98.07 and 98.45% for RF, CNN and ANN, respectively.

Deep Learning Framework for Liver Tumor Segmentation

“…Another study highlights the use of deep learning in segmenting and classifying liver tumors [14]. Traditional image processing, computer vision, and machine learning approaches classify the features extracted from CT images [15]. These methods extract features such as intensities of pixels, color, texture, size, and shape of tumors from liver CT scans and then employ a classifier method on these features to determine segmented images.…”