The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Medical diagnosis is an exciting are of research and many researchers have been working on the application of Artificial Intelligence techniques to develop disease recognition systems. They are analysing currently available information and also biochemical data collecting from clinical laboratories and experts for identifying pathological status of the patient. During the process of diagnosis, the...
In this paper, we introduce a method to find useful markers from sensor arrays which have massive sensing points and diagnose liver cancer based on machine learning algorithms which are neural network and fuzzy neural network. We obtain reliable results by using a learning ability and n-fold cross validation. For the verification of the proposed method, raw data of serums from 314 normal and 81 patients...
The aim of this study is to provide an automatic computational framework to assist clinicians in diagnosing Focal Liver Lesions (FLLs) in Contrast-Enhancement Ultrasound (CEUS). We represent FLLs in a CEUS video clip as an ensemble of Region-of-Interests (ROIs), whose locations are modeled as latent variables in a discriminative model. Different types of FLLs are characterized by both spatial and...
This article introduces hybrid automatic liver Parenchyma segmentation approach from abdominal CT images. The proposed approach consist of four main phases. Firstly, preprocessing phase which converts CT image into binary image using adaptive threshold method that examine the intensity values of the local neighborhood of each pixel. Then, the second phase is to apply multi-scale morphological operators...
A huge amount of microarray datasets are produced with big number of genes and small samples. Feature selection methods have become a very sharp tool to select the gene signatures from the whole gene set. In recent years, researchers are concerned much about the datasets containing samples of cancer as well as corresponding control tissues. However, few feature selection methods consider the effect...
For the diagnosis of liver cancer using a biopsy technique, pathologists¡¦ decisions are mainly based on the spatial and texture information of the biopsy images. However, the diagnostic accuracy strongly depends on the pathologist¡¦s knowledge and experience, that is, such diagnostic results are subjective. Hence, to make an objective and high accuracy diagnosis for the liver cancer in biopsy images,...
Liver cancer is one of the most popular cancer diseases and causes a large amount of death every year. Computer-aided liver analysis can help in reducing the risk of liver surgery and design treatment strategies. This paper develops a novel Computer-Aided Diagnosis (CAD) system using Contourlet Transform based feature extraction for automatic diagnosis of tumors in the liver. Evaluation of the CAD...
This study established a survival prediction model for liver cancer using data mining technology. The data were collected from the cancer registration database of a medical center in Northern Taiwan between 2004 and 2008. A total of 227 patients were newly diagnosed with liver cancer during this time. With literature review, and expert consultation, nine variables pertaining to liver cancer survival...
Support vector machine is a widely used tool in the field of image processing and pattern recognition. The parameters selection plays a significant role in support vector machine(SVM). This paper proposed an improved parameter optimization method based on traditional PSO optimizing algorithm by changing the fitness function in the traditional process. And this method has achieved better results which...
Identifying historical records of patients who are similar to the new patient could help to retrieve similar reference cases for predicting the clinical outcome of the new patient. Amongst different potential applications, this study illustrates use of patient similarity in predicting survival of patients suffering from hepatocellular carcinoma (HCC) treated with locoregional chemotherapy. This study...
We propose a novel method for detecting characteristic informative phenotype patterns from biomedical images. By building a metric space quantifying the difference between images, we learn the distributions of different classes, and then detect the characteristic regions using graph partition. We show that the detected regions are statistically significant. Our approach can also be used for designing...
Patient specific 3D finite element models have been developed using 4DCT (3D + time) image data for 5 liver cancer patients. Each model consists of the liver, tumors, left and right kidneys, stomach, spleen and body. Breathing motion of the liver, spleen and body is found and applied as displacement boundary conditions in the model. Sliding of the liver relative to the surrounding tissues is modeled...
The goal of this paper is to establish the error propagation model of the ultrasound-guided robot for liver cancer coagulation therapy, which consists of ultrasound machine, image-guided software subsystem, position tracking unit and needle-driven robot. The target of tumor is transformed to robot coordinate frame to let the robot move to the target. The transformation includes three dimension ultrasound...
Data mining algorithms are commonly used for cancer classification. Prediction models were widely used to classify cancer cells in human body. This paper focuses on finding small number of genes that can best predict the type of cancer. From the samples taken from several groups of individuals with known classes, the group to which a new individual belongs to is determined accurately. The paper uses...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.