Mathematical Morphology Method Research Articles

QRS detection by lifting scheme constructing multi-resolution morphological decomposition.

QRS complex detecting algorithm is core of ECG auto-diagnosis method and deeply influences cardiac cycle division for signal compression. However, ECG signals collected by noninvasive surface electrodes areusually mixed with several kinds of interference, and its waveform variation is the main reason for the hard realization of ECG processing. This paper proposes a QRS complex detecting algorithm based on multi-resolution mathematical morphological decomposition. This algorithm possesses superiorities in R peak detection of both mathematical morphological method and multi-resolution decomposition. Moreover, a lifting constructing method with Maximizationupdating operator is adopted to further improve the algorithm performance. And an efficient R peak search-back algorithm is employed to reduce the false positives (FP) and false negatives (FN). The proposed algorithm provides a good performance applying to MIT-BIH Arrhythmia Database, and achieves over 99% detection rate, sensitivity and positive predictivity, respectively, and calculation burden is low. Therefore, the proposed method is appropriate for portable medical devices in Telemedicine system.

Meso-damage cracking characteristics analysis for rock and soil aggregate with CT test

The aim of this paper is to investigate the damage cracking characteristics of rock and soil aggregate (RSA) by X-ray computed tomography (CT) under uniaxial compressive loading. The mean CT value for the region of interest (ROI) is used to analyze the cracking characteristics. Also, the mathematical morphology method based on the image threshold segmentation is used to obtain characteristic parameters of cracks to describe the cracking evolution of RSA. Results show that the elastic mismatch between rock blocks and soil matrix is the primary reason for RSA cracking. The mean CT value for the RSA specimen, rock block inclusions, and their adjacent soil regions decreases with the increasing stress level. However, it is more sensitive for block inclusions than soil regions. Using the image segmentation method, length, area and mean width of cracks obey to power function distribution. Crack statistical characteristics are closely related to the rock block’s distribution and morphology. These results may be useful to reveal the mesoscopic cracking mechanism, establish meso-damage evolution equation, and constitutive relation for RSA.

WE‐D‐9A‐04: Improving Multi‐Modality Image Registration Using Edge‐Based Transformations

Purpose:Multi‐modality deformable image registration (DIR) for head & neck (HN) radiotherapy is difficult, particularly when matching computed tomography (CT) scans with magnetic resonance imaging (MRI) scans. We hypothesized that the ‘shared information’ between images of different modalities was to be found in some form of edge‐based transformation, and that novel edge‐based DIR methods might outperform standard DIR methods.Methods:We propose a novel method that combines gray‐scale edge‐based morphology and mutual information (MI) in two stages. In the first step, we applied a modification of a previously published mathematical morphology method as an efficient gray scale edge estimator, with denoising function. The results were fed into a MI‐based solver (plastimatch). The method was tested on 5 HN patients with pretreatment CT and MR datasets and associated follow‐up weekly MR scans. The followup MRs showed significant regression in tumor and normal structure volumes as compared to the pretreatment MRs. The MR images used in this study were obtained using fast spin echo based T2w images with a 1 mm isotropic resolution and FOV matching the CT scan.Results:In all cases, the novel edge‐based registration method provided better registration quality than MI‐based DIR using the original CT and MRI images. For example, the mismatch in carotid arteries was reduced from 3–5 mm to within 2 mm. The novel edge‐based method with different registration regulation parameters did not show any distorted deformations as compared to the non‐realistic deformations resulting from MI on the original images. Processing time was 1.3 to 2 times shorter (edge vs. non‐edge). In general, we observed quality improvement and significant calculation time reduction with the new method.Conclusion:Transforming images to an ‘edge‐space,’ if designed appropriately, greatly increases the speed and accuracy of DIR.

IDENTIFICATION OF EXUDATES USING FUZZY MATHEMATICAL MORPHOLOGY

Diabetic Retinopathy is the damage to the retina ca used by complication and the most common cause of blindness in Thailand. Retinal image is essential f or expert ophthalmologists to diagnose diseases. Several of method can achieve good performance on retinal feature are clearly visible. Unfortunately, the color retinal image in Thailand are low-resolution images. The existing method cannot identified lowresolution image. Therefore, this study is part of a larger effort to develop a new method for identification of exudates in low-resolution retina l image. In this study a fuzzy mathematical morphol ogy based on fuzzy logical operator and mathematical mo rphology method is presented. The color retinal image are segmented by using fuzzy logical operator following key preprocessing step, i.e., color normalization, contrast enhancement, noise removal and color space selection. Afterward, a segmentatio n using mathematical morphology method was applied in this step. This enables its difference in our methods compared to other approach and the methods can achieve good performance even on lowresolution retinal images. Respect to the experimental results, the results ob tained with fuzzy mathematical morphology better than the ones obtained with the fuzzy logical operator only method.

Motorcyclist's Helmet Wearing Detection Using Image Processing

Motorcycle accidents have been rapidly growing throughout the years in many countries. Due to various social and economic factors, this type of vehicle is becoming increasingly popular. The helmet is the main safety equipment of motorcyclists but many drivers do not use it. If a motorcyclist is without helmet an accident can be fatal. This paper presented an automatic method for vehicle detection, motorcycles classification on public roads and a system for automatic detection of motorcyclists without helmet. For processing, in first step, we detect vehicles that moving real-time by extracting back ground out from front ground using back subtraction then enhancing it using threshold and mathematical morphology method. In the second step, we classify between motorcycle and other vehicles. Area is applied for feature extraction and neural network is applied for classification. In the final step, Hough transform is applied for detecting a helmet. From the experimental results, the accuracy rates of the motorcycle classification and helmet detection were 98.22% and 77%, respectively.

Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach.

Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time.

AUTOMATIC DETECTION OF OPTIC DISC AND BLOOD VESSELS FROM RETINAL IMAGES USING IMAGE PROCESSING TECHNIQUES

Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily.

The Research on Online Measurement System of Stone Slab Based on Improved Sobel Operator

A method of online measurement system of stone slab based on Sobel operator improvement is proposed in the paper in order to improve accuracy and productivity of stone slab measurement. Through adding Sobel operator model, they are combined with mathematical morphology method to improve the accuracy of boundary extraction of irregular stone slab and by establishing camera calibration theory formula a relation between world coordinate and image coordinate is set up. The result show that the method can implement online measurement of stone slab and the accuracy of this system can satisfy requirements of stone production industry.

THE APPLICATION OF DATABASE TECHNOLOGIES TO ANALYSIS OF ROCK STRUCTURE IMAGES

The aim of this paper is to present a possibility to use an information technology in the form of databases for processing and analyzing large image sets based on methods of image analysis and mathematical morphology. Up to now the use of databases in the image analysis process has been reduced to storing large amounts of data in the form of images. However, all transformations and analyses of such sets are made on user's computers. This requires a large data set (images) to be sent by network each time, and also it may possess the problems resulting from managing such large amounts of analyzed photographs on a computer. The proposed approach completely eliminates these problems by moving all transformations of image analysis to a database platform. For this purpose a set of routines realizing transformations of the image analysis and mathematical morphology was developed. The proposed approach allows the unification of the image processing and analysis area and advanced statistical analyses of obtained parameters describing geometrical sizes of objects on photographs. The proposed methodology was illustrated by practical realization of two measurement types for a simple structure of copper concentrate and more complicated, from the point of view of image analysis, structures such as dolomites from Redziny and Laskowa Gora and sandstones from Tumlin and Wiśniowka.

Retinal vessel extraction by means of motion contrast, matched filter and combined corner-edge detector

The microvasculature network of retina plays an important role in understanding of the retinal function and diagnosis of many diseases. Although it is possible to noninvasively acquire diffraction-limited resolution retinal images at microscopic cellular level, noises and other structures still make it difficult for diagnosis. In this paper, a new vessel extraction method is introduced. First, we use motion contrast method to trace the motion of the blood components and get the main vessel contour. Second, an improved matched filter method is applied to extract the vessel contour while the single-side edges are eliminated. Then, the combined corner/edge detector is adopted to eliminate the elongated fragments caused by the motion artifacts. Finally, we use mathematical morphology method to dilate the edges of vessels acquired in last step and obtain the exact contour of the vessels. The contrast of the vessels is significantly enhanced and the noises as well as other structures are effectively eliminated.

Method of Multiobject Detecting and Tracking Based on DM643

The technology of moving objects detection has become an important research subject for its extensive application prospect. In this paper, it is presented that interframe difference algorithm and background difference algorithm are combined to update the background. The algorithm can deal with the flaw of background difference algorithm. The mathematical morphology method is employed to denoise the image, which may be helpful to improve the accuracy of the detection. The Pyramid algorithm is used to compress each frame data of video sequence. Then, the detecting and tracking of moving objects are tested on the hardware platform (DM643) and the software frame (RF5). The running speed is about 3 times faster than before. The result shows that the accuracy demanded by the detection is met. This method can provide a useful reference for similar application.

Selected parameters of the corneal deformation in the Corvis tonometer.

IntroductionContemporary ophthalmology knows many methods of measuring intraocular pressure, namely the methods of non-contact and impression applanation tonometry. In non-contact applanation tonometers, e.g. the Corvis, the corneal flattening is caused by an air puff. Image registration of the corneal deflection performed by a tonometer enables to determine other interesting biomechanical parameters of the eye, which are not available in the tonometer. The measurement of new selected parameters is presented in this paper.Material and methodImages with an M × N × I resolution of 200 × 576 × 140 pixels were acquired from the Corvis device in the source recording format *.cst. A total of 13'400 2D images of patients examined routinely in the Clinical Department of Ophthalmology, in District Railway Hospital in Katowice, Poland, were analysed in accordance with the Declaration of Helsinki. A new method has been proposed for the analysis of corneal deflection images in the Corvis tonometer with the use of the Canny edge detection method, mathematical morphology methods and context-free operations.ResultsThe resulting image analysis tool allows determination of the response of the cornea and the entire eyeball to an air puff. The paper presents the method that enables the measurement of the amplitude of curvature changes in the frequency range from 150 to 500 Hz and automatic designation of the eyeball movement direction. The analysis of these data resulted in 3 new features of dynamics of the eye reaction to an air puff. Classification of these features enabled to propose 4 classes of deformation. The proposed algorithm allows to obtain reproducible results fully automatically at a time of 5 s per patient using the Core i5 CPU M460 @ 2.5GHz 4GB of RAM.ConclusionsThe paper presents the possibility of using a profiled algorithm of image analysis, proposed by the authors, to measure additional cornea deformation parameters. The new tool enables automatic measurement of the additional new parameters when using the Corvis tonometer. A detailed clinical examination based on this method will be presented in subsequent papers.

Implementation of ECG QRS complex detector for Body Sensor Networks

This paper aims to present a mathematical morphological method to remove baseline wandering and background noise is removed from original ECG signal. Then the multipixel modulus accumulation is employed to act as a low-pass filter to enhance the QRS complex and improve the signal-to-noise ratio. The performance of the algorithm is evaluated with standard MIT-BIH arrhythmia database. Corresponding power and area efficient VLSI architecture is designed and implemented on a commercial nano-FPGA. High detection rate and high speed demonstrate the effectiveness of the proposed detector. Index Terms: Body sensor networks (BSNs), electrocardiogram (ECG) sensor, field-programmable gate array (FPGA), mathematical morphology, QRS detection, very-large-scale integration (VLSI) architecture

QR Code Image Correction based on Corner Detection and Convex Hull Algorithm

Since the angular deviation produced when shooting a QR code image by a camera would cause geometric distortion of the QR code image, the traditional algorithm of QR code image correction would produce distortion. Therefore this paper puts forward the algorithm which combines corner detection with convex hull algorithm. Firstly, binaryzation of the collected QR code image with uneven light is obtained by the methods of local threshold and mathematical morphology. Next, the outline of the QR code and the dots on it are found and the distorted image is recovered by perspective collineation, according to the algorithm raised by this paper. Finally, experimental verification is made that the algorithm raised by this paper can correctly find the four apexes of QR code and achieves good effects of geometric correction. It will also significantly increase the recognition rate of seriously distorted QR code images

Data Mining Approach to Image Feature Extraction in Old Painting Restoration

Abstract In this paper a new approach to image segmentation was discussed. A model based on a data mining algorithm set on a pixel level of an image was introduced and implemented to solve the task of identification of craquelure and retouch traces in digital images of artworks. Both craquelure and retouch identification are important steps in art restoration process. Since the main goal is to classify and understand the cause of damage, as well as to forecast its further enlargement, a proper tool for a precise detection of the damaged area is needed. However, the complex nature of the pattern is a reason why a simple, universal detection algorithm is not always possible to be implemented. Algorithms presented in this work apply mining structures which depend of expandable set of attributes forming a feature vector, and thus offer an elastic structure for analysis. The result obtained by our method in craquelure segmentation was improved comparing to the results achieved by mathematical morphology methods, which was confirmed by a qualitative analysis.

Perceptual color hit-or-miss transform: application to dermatological image processing

The hit-or-miss transform is a mathematical morphological processing designed to find objects in image. Its extension to grayscale domain is not unique, but Barat’s method is the most appropriate to find specific objects with bandwidth in space and color evolutions. This tolerance is possible with the use of non-flat structuring elements. In this paper, a color extension of the Barat’s method is presented. For this purpose, a new mathematical morphology method, based on the concept of convergence in the CIELAB space and where the definition of non-flat structuring elements is possible, is used. A comparison with the existing approaches in the literature is done. Results are given and commented on synthetic and real images.

Improved Watershed Algorithm Based on Morphology and Distance Transform

In this paper we proposed an improved watershed algorithm for the quasi-circle overlapping images of the bars end face. According to the classical watershed algorithm, which often causes over-segmentation, the improved algorithm does a series of pretreatment with the original image, such as sobel filter. With the gradient operator and mathematical morphology method, we firstly obtain the smooth image of the forced local maximum marks. Then, on the basis of the quasi-circle characteristic of the target image, we proceed to maximize the erosion with circular structure in order to prevent under-segmentation. Finally, we use the watershed algorithm to segment the gray image based on distance transform. So we can separate the target from each other to achieve the accurate counting purpose. By using the proposed algorithm in the article, we obtain satisfactory segmentation results of the quasi-circle overlapping image of the bars end face image.

RISK ASSESSMENT BASED ON MATHEMATICAL MORPHOLOGY OF LANDSCAPE MODELS (CASE STUDY OF THERMOKARST PLAINS)

The paper presents a new approach for risk assessment of impact of thermokarst processes on engineering structures; it is based on the methods of mathematical morphology of landscape. The paper presents the results of the investigation of irregular (non-circular shape) thermokarst lakes. Remote sensing images of reference plots with thermokarst lakes were digitized; then, theoretical assumptions were applied and the simulation results were compared with empirical data to prove convergence. The analysis of the obtained results showed general agreement of empirical and theoretical data.

A Color-Texture Based Segmentation Method To Extract Object From Background

Extraction of flower regions from complex background is difficult task, it is an important part of flower image retrieval, and recognition .Image segmentation denotes process of partitioning an image into distinct regions. A large variety of different segmentation approaches for images have been developed. Image segmentation plays an important role in image analysis. According to several authors, segmentation terminates when the observers goal is satisfied. For this reason, unique method that can be applied to all possible cases does not yet exist. This paper studies the flower image segmentation in complex background. Based on the visual characteristics differences of the flower and the surrounding objects, the flower from different backgrounds are separated into single set of flower image pixels. The segmentation methodology on flower images consists of five steps. Firstly, the original image of RGB space is transformed into Lab color space. In the second step a component of Lab color space is extracted. Then segmentation by two-dimension OTSU of automatic threshold in a- channel is performed. Based on the color segmentation result, and the texture differences between the background image and the required object, we extract the object by the gray level co-occurrence matrix for texture segmentation. The GLCMs essentially represent the joint probability of occurrence of grey-levels for pixels with given spatial relationship in defined region. Finally, the segmentation result is corrected by mathematical morphology methods. The algorithm was tested on plague image database and the results prove to be satisfactory. The algorithm was also tested on medical images for nucleus segmentation.

Cotton and Bast Fiber Image Segmentation Based on Mathematical Morphology

Traditional cotton and bast fiber detection using artificial methods. Image processing techniques have been applied to the fiber detection and improve the automation and recognition efficiency of the detection. Image segmentation is the basic one of the steps for fiber identification. This paper using gradient edge detection method to segment the cotton and bast fiber longitudinal morphological image, and using morphological reconstruction operation method to the cross sectional fiber image. Both two kinds of images can be segmented by mathematical morphology method.