Kehong Yuan

Reducing microarray data via nonnegative matrix factorization for visualization and clustering analysis

In microarray data analysis, each gene expression sample has thousands of genes and reducing such high dimensionality is useful for both visualization and further clustering of samples. Traditional principal component analysis (PCA) is a commonly used method which has problems. Nonnegative Matrix Factorization (NMF) is a new dimension reduction method. In this paper we compare NMF and PCA for dimension reduction. The reduced data is used for visualization, and clustering analysis via k-means on 11 real gene expression datasets. Before the clustering analysis, we apply NMF and PCA for reduction in visualization. The results on one leukemia dataset show that NMF can discover natural clusters and clearly detect one mislabeled sample while PCA cannot. For clustering analysis via k-means, NMF most typically outperforms PCA. Our results demonstrate the superiority of NMF over PCA in reducing microarray data.

Volume-Based Features for Detection of Bladder Wall Abnormal Regions via MR Cystography

This paper proposes a framework for detecting the suspected abnormal region of the bladder wall via magnetic resonance (MR) cystography. Volume-based features are used. First, the bladder wall is divided into several layers, based on which a path from each voxel on the inner border to the outer border is found. By using the path length to measure the wall thickness and a bent rate (BR) term to measure the geometry property of the voxels on the inner border, the seed voxels representing the abnormalities on the inner border are determined. Then, by tracing the path from each seed, a weighted BR term is constructed to determine the suspected voxels, which are on the path and inside the bladder wall. All the suspected voxels are grouped together for the abnormal region. This work is significantly different from most of the previous computer-aided bladder tumor detection reports on two aspects. First of all, the T1-weighted MR images are used which give better image contrast and texture information for the bladder wall, comparing with the computed tomography images. Second, while most previous reports detected the abnormalities and indicated them on the reconstructed 3-D bladder model by surface rendering, we further determine the possible region of the abnormality inside the bladder wall. This study aims at a noninvasive procedure for bladder tumor detection and abnormal region delineation, which has the potential for further clinical analysis such as the invasion depth of the tumor and virtual cystoscopy diagnosis. Five datasets including two patients and three volunteers were used to test the presented method, all the tumors were detected by the method, and the overlap rates of the regions delineated by the computer against the experts were measured. The results demonstrated the potential of the method for detecting bladder wall abnormal regions via MR cystography.

An Adaptive Window-Setting Scheme for Segmentation of Bladder Tumor Surface via MR Cystography

This paper proposes an adaptive window-setting scheme for noninvasive detection and segmentation of bladder tumor surface in T_1 -weighted magnetic resonance (MR) images. The inner border of the bladder wall is first covered by a group of ball-shaped detecting windows with different radii. By extracting the candidate tumor windows and excluding the false positive (FP) candidates, the entire bladder tumor surface is detected and segmented by the remaining windows. Different from previous bladder tumor detection methods that are mostly focusing on the existence of a tumor, this paper emphasizes segmenting the entire tumor surface in addition to detecting the presence of the tumor. The presented scheme was validated by ten clinical T1-weighted MR image datasets (five volunteers and five patients). The bladder tumor surfaces and the normal bladder wall inner borders in the ten datasets were covered by 223 and 10 491 windows, respectively. Such a large number of the detecting windows makes the validation statistically meaningful. In the FP reduction step, the best feature combination was obtained by using receiver operating characteristics or ROC analysis. The validation results demonstrated the potential of this presented scheme in segmenting the entire tumor surface with high sensitivity and low FP rate. This study inherits our previous results of automatic segmentation of the bladder wall and will be an important element in our MR-based virtual cystoscopy or MR cystography system.

On α-divergence based nonnegative matrix factorization for clustering cancer gene expression data

OBJECTIVE Nonnegative matrix factorization (NMF) has been proven to be a powerful clustering method. Recently Cichocki and coauthors have proposed a family of new algorithms based on the alpha-divergence for NMF. However, it is an open problem to choose an optimal alpha. METHODS AND MATERIALS In this paper, we tested such NMF variant with different alpha values on clustering cancer gene expression data for optimal alpha selection experimentally with 11 datasets. RESULTS AND CONCLUSION Our experimental results show that alpha=1 and 2 are two special optimal cases for real applications.

A NOVEL FUZZY C-MEANS ALGORITHM AND ITS APPLICATION

The accurate and effective algorithm for segmenting image is very useful in many fields, especially in medical images. In this paper we introduced a novel method that focuses on segmenting the brain MR Image that is important for neural diseases. Because of many noises embedded in the acquiring procedure, such as eddy currents, susceptibility artifacts, rigid body motion and intensity inhomogeneity, segmenting the brain MR image is a difficult work. In this algorithm, we overcame the inhomogeneity shortage, by modifying the objective function by compensating its immediate neighborhood effect using Gaussian smooth method for decreasing the influence of the inhomogeneity and increasing the segmenting accuracy. Using simulate image and clinical MRI data, experiments show that our proposed algorithm is effective.

Pre-Processing of CT Brain Images for Content-Based Image Retrieval

In this paper, we propose an effective pre-processing method of CT brain images to provide consistent feature for content-based medical image retrieval. The key steps in pre-processing include cutting out background region, using ellipse to correct lean imaging angle, and normalization. We take vast CT brain images as experimental data to evaluate the method. Experimental results show the effect of pre-processing.

Brain CT image database building for computer-aided diagnosis using content-based image retrieval

Content-based image retrieval for medical images is a primary technique for computer-aided diagnosis. While it is a premise for computer-aided diagnosis system to build an efficient medical image database which is paid less attention than that it deserves. In this paper, we provide an efficient approach to develop the archives of large brain CT medical data. Medical images are securely acquired along with relevant diagnosis reports and then cleansed, validated and enhanced. Then some sophisticated image processing algorithms including image normalization and registration are applied to make sure that only corresponding anatomy regions could be compared in image matching. A vector of features is extracted by non-negative tensor factorization and associated with each image, which is essential for the content-based image retrieval. Our experiments prove the efficiency and promising prospect of this database building method for computer-aided diagnosis system. The brain CT image database we built could provide radiologists with a convenient access to retrieve pre-diagnosed, validated and highly relevant examples based on image content and obtain computer-aided diagnosis.

Semantic feature extraction for brain CT image clustering using nonnegative matrix factorization

Brain computed tomography (CT) image based computeraided diagnosis (CAD) system is helpful for clinical diagnosis and treatment. However it is challenging to extract significant features for analysis because CT images come from different people and CT operator. In this study, we apply nonnegative matrix factorization to extract both appearance and histogram based semantic features of images for clustering analysis as test. Our experimental results on normal and tumor CT images demonstrate that NMF can discover local features for both visual content and histogram based semantics, and the clustering results show that the semantic image features are superior to low level visual features.

Diagnosis system of computer-aided brain MRI using content-based image retrieval

Retrieving images is a useful tool to help radiologist to check medical image and diagnosis. In this paper, we propose a retrieval system of computer aided brain magnetic resonance imaging diagnosis , which integrated archiving and communication system, feature extraction and storage, feature match, and user interfaces. The system can be access in three ways, (i) using our system including PACS and image retrieval system, (ii)embedded the software into current PACS, and (iii) internet connection to submit an image as a query, and receive in return a set of images from the the web system. The system utilizes sophisticated image processing algorithms, combined with robust database technology and state-of-the-art web-enabled hardware, and packages these capabilities into a tightly-integrated diagnosis environment for the radiologist. This paper outlines the architecture, algorithms, and the test/verification procedures that our system has implemented.

Region Feature Extraction of Brain CT Image for Classification

Disease categorization based on medical image is a very challenge problem, because disease anatomical distribution on image is complex. In this paper, we describe an approach of region feature extraction, which is to partition the image into different regions and extract the local features in each region, to classify different diseases. Our experiments are based on a database consisting of 50 samples with two different disease types(stroke and tumor). The experimental results demonstrate our method is effective in classification precision comparing with Gabor feature extraction.