Detecting the Media-adventitia Border in Intravascular Ultrasound Images through a Classification-based Approach.

The detection of the media-adventitia (MA) border in intravascular ultrasound (IVUS) images is essential for vessel assessment and disease diagnosis. However, it remains a challenging task, considering the existence of plaque, calcification, and various artifacts. In this article, an effective method based on classification is proposed to extract the MA border in IVUS images.

Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images.

Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique.

A Novel Feature Selection Method Based on Extreme Learning Machine and Fractional-Order Darwinian PSO.

The paper presents a novel approach for feature selection based on extreme learning machine (ELM) and Fractional-order Darwinian particle swarm optimization (FODPSO) for regression problems. The proposed method constructs a fitness function by calculating mean square error (MSE) acquired from ELM. And the optimal solution of the fitness function is searched by an improved particle swarm optimization, FODPSO.

[The study of SVM-based recognition of particles in urine sediment].

This article used support vector machine (SVM) algorithm to recognize the particles in urine sediment in this paper. After feature extraction, cross-validation method and the contour chart of the accuracy were implemented to select the kernel function and the parameters of SVM, and according to the characteristics of SVM classifier and sample data, Multi-SVMs with two-level-classifier was successfully designed and A classification matrix was eventually obtained. The evaluation by using clinical data and comparative results with the artificial neural network have demonstrated that the proposed algorithm gets better results.

[An autofocus algorithm based on principal component analysis].

After extracting definition measurements by different focus functions from the images, a novel autofocus algorithm which combines them with principal component analysis (PCA) and considers the first principal component as the final measurement, is presented in this paper. The experiment results with 70 groups of images show that this algorithm increases the difference between the definition measurements of the images and provides higher focusing accuracy in comparison with other single ones.

[Design and development of the DSA digital subtraction workstation].

According to the patient examination criterion and the demands of all related departments, the DSA digital subtraction workstation has been successfully designed and is introduced in this paper by analyzing the characteristic of video source of DSA which was manufactured by GE Company and has no DICOM standard interface. The workstation includes images-capturing gateway and post-processing software. With the developed workstation, all images from this early DSA equipment are transformed into DICOM format and then are shared in different machines.

[A microscopic image mosaicing algorithm based on normalized moment of inertia].

A fast microscopic image mosaicing method is proposed in this paper by making a study of the mosaic methods and the characteristics of microscopic images. In the paper, invariant local features based on normalized moment of inertia (NMI) are used to select the matching points and calculate the spatial translation. The experimental results demonstrate that this algorithm can achieve fast, effective microscopic image mosaicing.

[Design and implementation of a system for transforming the NEMA 2.0 images into DICOM 3.0 images].

This paper introduces the design and implementation of a system which can get the NEMA2.0 image data from the hard disks of the imaging equipments directly,then analyzes and transforms these image data into the DICOM3.0 image data and sends them to the image server.

[Geometric active contour model with color and intensity priors for medical image segmentation].

A new algorithm using the geometric active contour model with the fusion of color and intensity priors to segment medical images is presented in this paper. The prior knowledge used here are firstly defined in different color spaces and represented as thresholds searched by the genetic algorithm. Then the prior knowledge is merged into active contour model with its contour evolution by the level set technique.

[Research progresses on deformable model in medical image processing].

In recent years, image processing technique based on deformable models has been widely used in the field of medical image processing and analysis. This survey demonstrates the principle of two kinds of basic deformable models, i.e.