Automatic Liver Segmentation from CT Images Using Single-Block Linear Detection

Abstract

Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity's impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods have been designed to overcome these challenges, but these methods still need to be improved to obtain the desired segmentation precision. In this paper, a fast algorithm is proposed for liver extraction from CT images with single-block linear detection. The proposed method does not require iteration; thus, the computational time and complexity are decreased enormously. In addition, the initialization is not crucial in the algorithm, so the algorithm's robustness and specificity are improved. The experimental evaluation of the proposed method revealed effective segmentation in normal and abnormal (liver hemangioma and liver cancer) abdominal CT images. The average sensitivity, accuracy, and specificity for liver cancer are 96.59%, 98.65%, and 99.03%, respectively. The results of image segmentation approximate the manual segmentation results by the technical doctor. Moreover, our method shows superior flexibility to newly published method with comparable performance. The advantage of our method is verified with experimental results, which is described in detail.

Liver and Tumor Segmentation Techniques for CT Abdominal Images

“…In [10], a Single-Block Linear Detection Algorithm (SBLDA) algorithm is used for extracting liver from the CT images. The edge of the liver is extracted by applying morphological operations by using confidence matrix which is calculated by using SBLDA technique.…”

Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

“…On the other hand, deep learning methods usually use convolutional neural networks (CNN) that consist of a number of convolutional layers for extracting low-level and high-level features for the liver CT images and fully connected layers to encode a compact feature set for the segmentation process. For the task of liver segmentation, a preliminary step in many CAD systems for liver cancer [2] and liver fibrosis [3], different traditional and deep learning methods have been applied. For example, Barstugan et al [4] used a super-pixel linear iterative clustering approach and AdaBoost algorithm to segment the liver, achieving a DSC of 92.13% on 16 abdomen CT test images.…”

Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy

“…Over the years, numerous techniques for segmenting a 3D liver from CT have been proposed. These techniques relied on different features being extracted from imaging data, and can be generally classified into those based on thresholding [ 11 , 15 ], region growing (RG) [ 13 , 16 , 17 ], graph processing [ 5 , 18 , 19 , 20 , 21 , 22 ], machine learning [ 23 , 24 ], level-set [ 25 ], and deformable model [ 26 ]. Detailed survey and discussion on the state of the art are presented in [ 27 , 28 ] and summarized as follows:…”

“…Subsequently, Bayesian classification was used to label individual pixels as either liver or non-liver a posteriori, based on their inferred type. Similarly, in [ 15 ] a single block linear detection algorithm (SBLDA) was proposed. The image was first preprocessed by median filter and morphological operator, whereby the block values and confidence matrix were respectively computed.…”