Segmentation of Liver Tumor Using Fast Greedy Snake Algorithm

Abstract

Back Ground: Liver tumors are a type of growth found in the liver which can be categorized as malignant or benign. It is also called as hepatic tumors. Early stage detection of tumor could be treated at a faster phase; if it is left undiagnosed it may lead to several complications. Traditional method adopted for diagnosis can be time consuming, error-prone and also requires an experts study. Hence a non invasive diagnostic method is required which overcomes the flaws of conventional method. Liver segmentation from CT images in post processing techniques not only is an essential prerequisite, but, by playing an important role in confirming liver function, pathological, and anatomical studies, is also a key technique for diagnosis of liver disease. Hence in the proposed study Fast greedy snakes algorithm in abdominal CT images were used for segmenting tumor portion. Aim & Objectives: The aim and objectives of study is: (i) to segment tumor region in the liver image using Fast Greedy Snakes Algorithm (FGSA); (ii) to extract the GLCM features from the segmented region; (iii) to classify the normal and abnormal liver image using neural network classifier. Methodology: The study involved a total of 30 normal and 30 abnormal Images from database. In the proposed study automated segmentation was performed using Fast Greedy Snakes (FGS) Algorithm and the features were extracted using GLCM method. Classification of normal and abnormal images was carried out using Back propagation Neural Network classifier. Result: The proposed FGS algorithm provides accurate segmentation in liver images. Statistical features like mean, kurtosis, correlation and Entropy showed a higher value for the normal image than liver tumor image. On the other hand, features like Skewness, Homogeneity, contrast, Energy and standard deviation showed a comparatively higher value for a liver tumor image than the normal. Statistical features such as Mean, Contrast, Homogeneity and standard deviation are statistically significant at [Formula: see text]. Features like correlation, entropy and energy exhibits significance at [Formula: see text]. The feature extracted values provided significant difference between the normal and abnormal liver images. The neural network classifier yields the sensitivity of 95.8%, sensitivity of 81.4% and achieved the overall accuracy of 92%. Conclusion: A most accurate, reliable and fast automated method was implemented to segment the liver tumor image using Fast Greedy snakes algorithm. Hence the proposed algorithm resulted in effective segmentation and the classifier could classify the normal and abnormal images with greater accuracy.

Automatic liver tumor segmentation and identification using fully connected convolutional neural network from CT images

“…The performance of the proposed FC‐CNN model is compared with an existing state‐of‐art method to claim superiority. The various methods considered for the comparison are Naïve Bayes, 30 SVM classifier, 12 KNN Classifier, 31 Mask R‐CNN, 32 2D CNN, 33 AdaBoost M1 classifier, 34 Random Forest method, 35 Hybrid CNN model, 5 ResUNet, 36 CNN, 15 GAN, 37 and FGS Method 38 . The comparison for accuracy, sensitivity, and specificity are tabulated in Table 2.…”