Survey on liver CT image segmentation methods.

The segmentation of liver using computed tomography (CT) data has gained a lot of importance in the medical image processing field. In this paper, we present a survey on liver segmentation methods and techniques using CT images, recent methods presented in the literature to obtain liver segmentation are viewed. Generally, liver segmentation methods are divided into two main classes, semi-automatic and fully automatic methods, under each of these two categories, several methods, approaches, related issues and problems will be defined and explained. The evaluation measurements and scoring for the liver segmentation are shown, followed by the comparative study for liver segmentation methods, pros and cons of methods will be accentuated carefully. In this paper, we concluded that automatic liver segmentation using CT images is still an open problem since various weaknesses and drawbacks of the proposed methods can still be addressed

Liver Tumour Detection for Ct Images

A cancer is an abnormal growth of cells typically derived from a single abnormal cell. Detection of liver tumour in early stage is important for the prevention of liver tumour. Many techniques have been developed for the detection of liver tumour from CT images. Treatment of any cancer mainly depends on tumour size and grading. Hepatocellular carcinoma is the most common type of liver cancer. Diagnosis involves CT scan of abdomen provides accurate results. Tumour segmentation in liver CT images is a challenging task. This paper deals with the detection of liver tumour from CT images. Implementation of FCM technique and some default tools help to detect the shape of tumour. Finally the tumour part is extracted from CT images and its exact position and shape is determined to calculate the abnormality are

Advanced image processing methods for automatic liver segmentation

This paper presents advanced methods of image segmentation suitable for automatic recognition of the human liver and its vessel system, but in general could be used to segment any organ or body tissue. The comparison of studied methods is being made in terms of segmentation quality and algorithm speed. The main criterion for quality evaluation of each selected method is the level of conformity between the automatically recognized boundary and the reference boundary specified by experienced user. For all the tests sequences of CT and MRI images were used

AUTOMATIC LIVER SEGMENTATION FROM CT SCANS USING INTENSITY ANALYSIS AND LEVEL-SET ACTIVE CONTOURS

Liver segmentation from CT scans is still a challenging task due to the liver characteristics in terms of shape and intensity variability. In this work, we propose an automatic segmentation method of the liver from CT data sets. The framework consists of three main steps: liver shape model localization, liver intensity range estimation and localized active contouring. We proposed an adaptive multiple thresholding technique to estimate the range of the liver intensities. First, multiple thresholding is used to extract the dense tissue from the whole CT scan. A localization step is then used to find the approximate location of the liver in the CT scan, to localize a constructed mean liver shape model. A liver intensity-range estimation step is then applied within the localized shape model ROI. The localized shape model and the estimated liver intensity range are used to build the initial mask. A level set based active contour algorithm is used to deform the initial mask to the liver boundaries in the CT scan. The proposed method was evaluated on two public data sets: SLIVER07 and 3D-IRCAD. The experiments showed that the proposed method is able to segment to liver in all CT scans in the two data sets accurately

Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Segmentation Model for Automatic Detection and Modelling of Liver Area from Medical Images

Tato diplomová práce se zaměřuje na popis klinických aspektů jaterní oblasti, dále pak uvádí nejčastěji využívané zobrazovací metody pro tuto oblast. Cílem této práce je vytvoření návrhu a implementace segmentačního modelu pro automatickou detekci jaterní oblasti z medicínských obrazů. Pro zvolení vhodné segmentační metody pro automatizovanou detekci jater je třeba nastudování odborné literatury týkající se aktuálně využívaných automatických segmentačních metod pro oblast jater. Navržený model pro automatickou detekci je následně testován pro variabilní obrazové podmínky. Testování probíhá na reálných CT obrazech jater. Následně je provedeno hodnocení robustnosti navrženého modelu. Posledním cílem této práce je detekce významných struktur z automaticky detekovaných obrazů jaterní oblasti. Proces návrhu a implementace modelu probíhá v prostředí MATLAB, pro zpracování výsledků je využito programu Microsoft Excel.This thesis focuses on describing the clinical aspects of the liver area and presents the most commonly used imaging methods for this area. This work aims to propose and implement a segmentation model for automatically detecting the liver area from medical images. A study of professional literature on currently used automatic segmentation methods for the liver area is necessary to select a suitable segmentation method for automated liver detection. The proposed model for automatic detection is subsequently tested for variable imaging conditions. Testing is performed on real CT liver images. Subsequently, an evaluation of the robustness of the proposed model is carried out. The final objective of this work is to detect significant structures from automatically detected liver area images. The model is designed and implemented in MATLAB, and Microsoft Excel is used for result processing.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn