Data Visualization for the Prediction of Liver Cancer Disease using Different Graphical Techniques (original) (raw)
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CAD System for Liver Diseases using Histological and Imaging features
The current work for characterization of liver disease has been carried out using histological and imaging data. The BUPA liver disorders dataset created by University of California, Irvine has been considered as histological data. The ultrasound images of hepatocellular carcinoma (HCC) and Hemangioma (HEM) lesions were taken from ultrasoundcases.info. Laws 'texture features were extracted from these images using Laws' masks of length 3. In CAD system design 1, histological classification of liver diseases has been carried out using SVM classifier. In CAD system design 2, liver disease classification has been carried out using imaging features. Finally in CAD system design 3, liver disease classifications have been carried out using both histological and imaging data features. It is observed that liver disease classification higher accuracy is obtained by combining histological and imaging features.
COMPUTER AIDED DIAGNOSIS FOR LIVER CANCER USING STATISTICAL MODEL
Liver Cancer is one of the most difficult cancer to cure and the number of deaths that it causes generally increasing. The signs and the symptoms of the liver cancer are not known, till the cancer is in its advanced stage. So, early detection is the main problem. If it is detected earlier then it can be helpful for the Medical treatment to limit the danger, but it is a challenging task due to the Cancer cell structure. Interpretation of Medical image is often difficult and time consuming, even for the experienced Physicians. Most traditional medical diagnosis systems founded needs huge quantity of training data and takes long processing time. Focused on the solution to these problems, a Medical Diagnosis System based on Hidden Markov Model (HMM) is presented. This paperdescribes a computer aided diagnosis system for liver cancer that detects the liver tumor at an early stage from the chest CT images. This automation process reduces the time complexity and increases the diagnosis confidence.
Image Processing Techniques as a Tool for the Analysis of Liver Diseases
Zenodo (CERN European Organization for Nuclear Research), 2023
Identification of diseases and their successful treatment is largely determined by early diagnosis. This allows you to both prevent the development of the disease and get rid of possible negative consequences. Various data can be used for these purposes. We are looking at medical imaging techniques. Microscopic images of the liver, where manifestations of fatty disease are possible, were chosen as the object of study. The paper summarizes the general scheme of the corresponding analysis, and presents the results on real images.
Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor
BioMed Research International
The liver is in charge of a plethora of tasks that are critical to healthy health. One of these roles is the conversion of food into protein and bile, which are both needed for digestion. Inhaled and possibly harmful chemicals are flushed from the body. It destroys numerous nutrients acquired through the gastrointestinal system and limits the release of cholesterol by utilizing vitamins, carbohydrates, and minerals stored in the liver. The body’s tissues are made up of tiny structures known as cells. Cells proliferate and divide in order to create new ones in the normal sequence of events. When an old or damaged cell has to be replaced, a new cell must be synthesized. In other circumstances, the procedure is a total and utter failure. If the tissues of dead or damaged cells that have been cleared from the body are not removed, they may give birth to nodules and tumors. The liver can produce two types of tumors: benign and malignant. Malignant tumors are more dangerous to one’s healt...
Detection of Abnormalities in Liver Using Image Processing Techniques
2021
The liver is the largest gland and largest internal organ in the human body. The abnormal growth of cell in the liver causes liver cancer which is also known as hepatic cancer where, Hepatocellular Carcinoma (HCC). Most of the peoples who have liver tumour were died due to the fact of inaccurate detection. The detection of this tumours is difficult and mostly found at advanced stage which causes life-threatening issues. Hence it is far essential to discover the tumour at an early stage. So, the principle intention of this project is to detect liver cancer at earlier stage using image processing technique. Computer-aided diagnosis from various medical imaging techniques can assist significantly in detecting liver cancer at a very early stage. This project presents an automated method of detecting liver cancer in abdominal CT images and classifying them using the support vector machine (SVM) algorithm. The proposed model consists of several stages where the image is first normalized a...
IDENTIFICATION OF DIFFERENT LIVER DISEASES BY USING IMAGERY TECHNIQUES: A REVIEW
The aim of this study is to characterize the different liver diseases which are related to infect liver tissue and other infected part like spleen, pancreas, and stomach. The methods used are preprocessing ,segmentation, feature extraction, texture analysis which is depend upon the collection of Computed Tomography(CT) scans, MRI(Magnetic Resonance Imaging) and Ultrasounds(US) of different patient collected from the hospitals. MRI is playing an important role in analyzing the liver disease patients due to its high soft tissue resolution, lack of ionizing radiation and ability to provide functional data. Imaging techniques like ultrasonography, CT scans, MRI are used to get the enhanced image of infected part of liver so that can easily characterized and detect the particular disease in liver patient. Diffusion Weighted Imaging (DWI) is used to identify the lesion in the liver and Perfusion Weighted Imaging (PWI) is used to identify the volume and functioning of the cirrhotic liver.
Diagnosis of Liver Neoplasms by Computational and Statistical Image Analysis
Gastroenterology Research, 2019
Background: Distinguishing well-differentiated hepatocellular carcinoma (WD-HCC), hepatocellular adenoma (HA) and non-neoplastic liver tissue (NNLT) solely on morphology is often challenging. The purpose of this study was to evaluate the use of computational image analysis to distinguish WD-HCC, HA and NNLT. Methods: Seventy-seven cases comprising of WD-HCC (n = 26), HA (n = 23) and NNLT (n = 28) were retrieved and reviewed. A total of 485 hematoxylin and eosin (H&E) photomicrographs (× 400, 0.09 µm 2) of WD-HCC (n = 183), HA (n = 173), NNLT (n = 129) and nine whole-slide scans (three of each diagnosis) were obtained, color deconvoluted and digitally transformed. Quantitative data including nuclear density, nuclear sphericity, nuclear perimeter, and nuclear eccentricity from each image were acquired. The data were analyzed by one-way analysis of variance (ANOVA) with Tukey post hoc test, followed by unsupervised and supervised (Chi-square automatic interaction detection (CHAID)) cluster analysis. Results: Unsupervised cluster analysis identified three well defined clusters of WD-HCC, HA and NNLT. Employing the four most discriminating nuclear features, supervised analysis was performed on a training set of 383 images, and validated on the remaining 102 test images. The analysis identified WD-HCC (sensitivity 100%, specificity 98%), HA (sensitivity 71%, specificity 85%) and NNLT (sensitivity 70%, specificity 86%). An analysis of whole-slide images identified WD-HCC with sensitivity and specificity of 100%. Conclusions: We have successfully demonstrated that computational image analysis of nuclear features can differentiate WD-HCC from non-malignant liver with high accuracy, and can be used to assist in the histopathological diagnosis of hepatocellular carcinoma.
Assessment on Liver Disease Classification using M
International Journal of Recent Technology and Engineering (IJRTE)
Chronic hepatic disease (CHD) is progressive fatal disease which is often asymptomatic. CHD has increased mortality and morbidity rate even in developed countries also. Invasive and non-invasive methods are used to classify and stage the CHD. In the research, using Ultra Sonographic images (US), clinical finding and laboratory findings for the staging of CHD is done. There are three stages of CHD which are Chronic Hepatitis, Compensated Cirrhosis and Decompensated cirrhosis. For invasive method, liver biopsy is done followed by histopathological examinations. Results of liver biopsy have some complications. So, non-invasive procedures are used as a safe alternative for liver biopsy.This paper presents current various methods of segmentation based on medical liver images. And also, this paper focuses on the work of various segmentation and classification methods that has been proposed to diagnosis many liver diseases.
Automated Focal Liver Lesion Staging Classification based on Haralick Texture Features and Multi-SVM
This paper proposes automated identification and classification of various stages of focal liver lesions based on the Multi-Support Vector Machine (Multi-SVM). The proposed system can be used to discriminate focal liver diseases such as Cyst, Hemangioma, and Hepatocellular carcinoma along with normal liver. The multi-class scenario is a composition of a series of two-class problems, using oneagainst-all which is the earliest and one of the most widely used implementations. We formulate the discrimination between cysts, cavernous hemangioma, hepatocellular carcinoma, and normal tissue as a supervised learning problem, and apply Multi-SVM to classify the diseases using Haralick local texture descriptors and histogram based features calculated from Regions Of Interest (ROIs), as input. Selection of ROI significantly impact the classification performances, thus we proposes an automatic ROI selection using Fuzzy c-means initialized by level set technique. For multi-class classification, we adopt the One-Against-All (OAA) method. The proposed Multi-SVM based CAD system using 10-fold cross validation yielded classification accuracy of 96.11% with the individual class accuracy of 97.78%, 95.56%, 93.33% and 97.78% for NOR, Cyst, HEM and HCC cases respectively. The proposed Multi-SVM based system is compared with the K-Nearest Neighbor (KNN) based approaches. Experimental results have demonstrated that the Multi-SVM based system greatly outperforms KNN-based approaches and other methods in the literature. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of focal liver lesion diseases.
2008
This paper is focused on current progress of our research in improving diagnosis value of ultrasound imaging in the context of diffuse liver diseases. Image features are computed on ultrasound images and these features are used to train a classifier. The classifier is able to distinguish between various pathology grades. Present study shows that, based on ultrasound images, steatosis can be accurately graded and a qualitative assessment can be made in case of fibrosis. Further improvements can be made if we include more patients and consider non-imagistic features like clinical and biochemical analysis of the patient.