Dinh Trinh - Academia.edu (original) (raw)

Papers by Dinh Trinh

CMCase (carboxymethyl cellulase) was an enzyme catalyzing hydrolysis of 1,4-β-glycoside bonds in ... more CMCase (carboxymethyl cellulase) was an enzyme catalyzing hydrolysis of 1,4-β-glycoside bonds in molecule cellulose. CMCases have a broad variety of applications in food, animal feed, brewing, paper pulp, detergent industries, textile industry, fuel, chemical industries, waste management and pollution treatment. CMCase is produced by liquid state fermentation (LSF) and solid state fermentation (SSF). In this study, we described optimal conditions and medium components for CMCase production by the basidiomycetes strain Peniophora sp. NDVN01 under solid state fermentation: the influence of substrate source, the ratio corncob/soybean, water amount, culture time, airflow, culture temperature, initial pH value in solid substrate, carbon source and nitrogen source. Corncob/soybean ratio (4/1), initial moisture content of 80% (v/w substrate), culture time of 7 days, airflow (4 g substrate/flask 250 ml), culture temperature of 28°C, intial pH of 7 and 1% (w/w) (NH4)2HPO4 were optimum for CM...

Iranian Journal of Biotechnology, 2013

Image Processing, IEEE International Conference, 2011

Medical images are often corrupted by random noise, leading to undesirable visual quality. Thus, ... more Medical images are often corrupted by random noise, leading to undesirable visual quality. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. In this paper, we propose a novel learning method for the reduction of Gaussian noise of Computed Tomography (CT) image and Rician noise of Magnetic Resonance Imaging (MRI) image based on a given

Lecture Notes in Computer Science, 2011

Medical images are often corrupted by random noise due to various acquisitions, transmission, sto... more Medical images are often corrupted by random noise due to various acquisitions, transmission, storage and display devices. Noise can seriously affect the quality of disease diagnosis or treatment. Image denosing is then a required task to ensure the quality of medical image analysis. In this paper, we propose a novel method for reducing some types of common noises in medical images by using a set of given standard images and a well-known machine learning technique namely the Support Vector Regression (SVR). Experimental results are carried out to demonstrate that our method can effectively denoise while preserving small details. A comparison is also performed to demonstrate the outperformance of the proposed technique in terms of both objective and subjective evaluations.

2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2012

ABSTRACT This paper introduces a fast and simple geometric solution for solving the problem of ex... more ABSTRACT This paper introduces a fast and simple geometric solution for solving the problem of example-based image superresolution with the advantages of well suppressing noise. Here, an image is considered as a set of small image patches, and super-resolution is performed on each patche with the help of a given database of low-resolution and high-resolution image patch pairs. For each given low-resolution patch, to estimate its corresponding high-resolution patch, a set of candidate highresolution patches is first searched from the database using a criteria based on Euclidean distance and statistical properties. Then, by considering each image patch as a point in a high dimensional space, the disered high-resolution patch is determined using the projection of the low-resolution point onto the convex hull of the candidate high-resolution points. Experiments are carried out to demonstrate the performance of the proposed method over some state-of-the-art methods.

2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012

In this paper, a novel example-based superresolution (SR) method is introduced. The objective is ... more In this paper, a novel example-based superresolution (SR) method is introduced. The objective is to estimate a high-resolution image from a single low-resolution image. By considering an image as a set of small image patches, our method is performed on each patch with the help of a given database of high and low-resolution image patch pairs. For each given low-resolution patch, its high-resolution version is considered as a sparse positive linear combination of the high-resolution patches from the database. The coefficients of this combination are referred to as the weights, and an optimal weight model is proposed to find this combination such that the high-resolution patch is consistent with the low-resolution patch while being similar to the best candidate high-resolution patches from the database. Experimental results show the good performance of our method over some state-of-the-art methods and confirm the efficiency of the proposed method.

The 2011 International Conference on Advanced Technologies for Communications (ATC 2011), 2011

Abstract Magnetic Resonance (MR) imaging is useful for medical diagnosis. However, MR images are ... more Abstract Magnetic Resonance (MR) imaging is useful for medical diagnosis. However, MR images are often corrupted by Rician noise, leading to undesirable visual quality. Based on the fact that many images can be acquired at nearly the same location, this paper ...

Gastrointestinal Endoscopy, 1988

The American Journal of Medicine, 1995