Zhao Yu-qian

Bio: Zhao Yu-qian is an academic researcher from Central South University. The author has contributed to research in topics: Image segmentation & Mathematical morphology. The author has an hindex of 3, co-authored 8 publications receiving 212 citations.

Medical Images Edge Detection Based on Mathematical Morphology

Abstract: Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, edge is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced at first, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms

Passive Detection of Copy-paste Tampering for Digital Image Forensics

Abstract: Copy-paste processing is a frequently used method to tamper an image. For JPEG format is the mostly employed image format, more likely the copied slice came from JPEG image. In this paper, we first introduce two copy-paste tampering Scenarios: one is conducted between JPEG images and saved in a loss less format, the other is also conducted between JPEG images, but is saved in the JPEG format. Then we analyze and simulate the detection method for the two tampering Scenarios. We detect the tampered region by computing the sum of absolute difference images between the tampered image and a resaved JPEG compressed image at different quality factors. Experimental results show that our method can effectively detect the tampered region in two tampering Scenarios.

A novel method for retinal vascular image enhancement

Abstract: Retinal vessels play an important role in the diagnostic procedure of retinopathyA new retinal vascular image enhancement method is proposedFirstly,the retinal image is enhanced preliminarily by using the optimal image features which are selected from the multi-scale white and dim image features extracted by multi-scale top-hat transformationAnd then according to the histogram distribution characteristics of retinal vascular gray image,the preliminary enhanced image is processed by linear stretching with histogram Gaussian curve fittingThe experimental results show that the proposed method improves the contrast and enhances the details of the retinal vascular images more effectively than the existing methods

Development of a Portable Blood Sugar Apparatus and GOD Enzyme Strip

Abstract: A pocket blood sugar apparatus tested by enzyme electrode, which was designed using carbon and silver plasma as conducting materials. Both the work and reference electrodes are applied to the parts of enzyme electrode. The glucose oxidase is taken as the medium of blood sugar measuring. And the range of measuring glucose is about 50mg/dL - 500mgl/dL. It has better linear for the results and fit coefficient arrives at 0.985. Its sensitivity of measurement is higher than current glucose biosensor obviously. A single chip microcomputer, AD mu C812, is used for central control processor of the instrument parts. It measures the output of microampere level currency, which is conduced by blood sugar reacting with the glucose oxidase on the enzyme electrode. And at the same time, the microampere level currency is amplified, processed. Then the results are displayed on LCD. The apparatus are better for measuring blood sugar, and the results are consistent with what the large biochemical instruments get

Liver CT image segmentation based on prior shape CV model

Abstract: CV model can not segment correctly under the condition that some essential information is missed partly or some parts of the objects are occluded.To solve this problem,a new model based on prior shape focusing on detecting occluded objects is proposed.The new model firstly constructs prior shape which is obtained by mathematical morphology combining with other algorithms,then integrates the prior shape into CV model functional through a novel signed distance function,in which the signed function is constructed by fast implemented boundary searching algorithm and region labeling.The proposed method is applied to segment liver from CT images,with noises around boundary or part of liver being occluded.Compared with the results of CV model,the experimental results show that the new model can detect occluded liver from CT images successfully.

A Review of Deep-Learning-Based Medical Image Segmentation Methods

Abstract: As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.

Comparative Study of Edge Detection Algorithms Applying on the Grayscale Noisy Image Using Morphological Filter

Abstract: In this paper, classified and comparative study of edge detection algorithms are presented. Experimental results prove that Boie-Cox, ShenCastan and Canny operators are better than Laplacian of Gaussian (LOG), while LOG is better than Prewitt and Sobel in case of noisy image. Subjective and objective methods are used to evaluate the different edge operators. The morphological filter is more important as an initial process in the edge detection for noisy image and used opening-closing operation as preprocessing to filter noise. Also, smooth the image by first closing and then dilation to enhance the image before the edge operators affect.

Adaptive fuzzy moving K-means clustering algorithm for image segmentation

Abstract: Image segmentation remains one of the major challenges in image analysis. Many segmentation algorithms have been developed for various applications. Unsatisfactory results have been encountered in some cases, for many existing segmentation algorithms. In this paper, we introduce three modified versions of the conventional moving k-means clustering algorithm called the fuzzy moving k-means, adaptive moving k-means and adaptive fuzzy moving k-means algorithms for image segmentation application. Based on analysis done using standard images (i.e. original bridge and noisy bridge) and hard evidence on microscopic digital image (i.e. segmentation of Sprague Dawley rat sperm), our final segmentation results compare favorably with the results obtained by the conventional k-means, fuzzy c-means and moving k-means algorithms. The qualitative and quantitative analysis done proved that the proposed algorithms are less sensitive with respect to noise. As such, the occurrence of dead centers, center redundancy and trapped center at local minima problems can be avoided. The proposed clustering algorithms are also less sensitive to initialization process of clustering value. The final center values obtained are located within their respective groups of data. This enabled the size and shape of the object in question to be maintained and preserved. Based on the simplicity and capabilities of the proposed algorithms, these algorithms are suitable to be implemented in consumer electronics products such as digital microscope, or digital camera as post processing tool for digital images.