Retinal Blood Vessel Extraction from Fundus Images Using Enhancement Filtering and Clustering

Screening of vision troubling eye diseases by segmenting fundus images eases the danger of loss of sight of people. Computer assisted analysis can play an important role in the forthcoming health care system universally. Therefore, this paper presents a clustering based method for extraction of retinal vasculature from ophthalmoscope images. The method starts with image enhancement by contrast limited adaptive histogram equalization (CLAHE) from which feature extraction is accomplished using Gabor filter followed by enhancement of extracted features with Hessian based enhancement filters. It then extracts the vessels using K-mean clustering technique. Finally, the method ends with the application of a morphological cleaning operation to get the ultimate vessel segmented image. The performance of the proposed method is evaluated by taking two different publicly available Digital retinal images for vessel extraction (DRIVE) and Child heart and health study in England (CHASE_DB1) databases using nine different performance matrices. It gives average accuracies of 0.952 and 0.951 for DRIVE and CHASE_DB1 databases, respectively.    

Self-correction of 3D reconstruction from multi-view stereo images

We present a self-correction approach to improving the 3D reconstruction of a multi-view 3D photogrammetry system. The self-correction approach has been able to repair the reconstructed 3D surface damaged by depth discontinuities. Due to self-occlusion, multi-view range images have to be acquired and integrated into a watertight nonredundant mesh model in order to cover the extended surface of an imaged object. The integrated surface often suffers from “dent” artifacts produced by depth discontinuities in the multi-view range images. In this paper we propose a novel approach to correcting the 3D integrated surface such that the dent artifacts can be repaired automatically. We show examples of 3D reconstruction to demonstrate the improvement that can be achieved by the self-correction approach. This self-correction approach can be extended to integrate range images obtained from alternative range capture devices

A thresholding based technique to extract retinal blood vessels from fundus images

Retinal imaging has become the significant tool among all the medical imaging technology, due to its capability to extract many data which is linked to various eye diseases. So, the accurate extraction of blood vessel is necessary that helps the eye care specialists and ophthalmologist to identify the diseases at the early stages. In this paper, we have proposed a computerized technique for extraction of blood vessels from fundus images. The process is conducted in three phases: (i) pre-processing where the image is enhanced using contrast limited adaptive histogram equalization and median filter, (ii) segmentation using mean-C thresholding to extract retinal blood vessels, (iii) post-processing where morphological cleaning operation is used to remove isolated pixels. The performance of the proposed method is tested on and experimental results show that our method achieve an accuracies of 0.955 and 0.954 on Digital retinal images for vessel extraction (DRIVE) and Child heart and health study in England (CHASE_DB1) databases respectively

Analysis of the crack growth behavior in a double cantilever beam adhesive fracture test using digital image processing techniques

Digital image processing (DIP) techniques offer interesting possibilities in various fields of science.Automated analyses may significantly reduce the necessary manpower for certain cumbersometasks. The analysis of large series of images may be done in less time, since automatedimage processing techniques are able to work efficiently and with constant quality 24h per day.In this work, a series of images obtained by a high-speed camera is analyzed in order to determinethe crack growth behavior during a double cantilever beam (DCB) test [1]. The presentwork represents a contribution to the effort of automatizing the crack growth measurement,comparing various different techniques which may later be optimized for a specific task.Detecting cracks automatically from test images obtained by a digital camera is a difficult task,since the quality of crack images depends on the test conditions. The roughness of the specimensurface, luminance condition, and the camera itself may influence the detection quality.The specimens tested in this work where painted with white colour since this was found to leadto the best contrast for crack detection. High accuracy may only be expected if a sufficientlyhigh resolution is acquired by the camera and if the available lens setup is optimized for thespecific task.The DCB test is performed in order to obtain the experimental compliance-crack length curveof a polymeric adhesive. Accurate and reliable crack length measurement is indispensable forthe generation of the previously mentioned compliance-crack length curves. It should be notedthat due to the lenses used, unlike shown by Ryu [2], the distance to the specimen is higher than800 mm. This distance has to be reduced by the use of a different lens setup in order to get abetter accuracy of the results. Nevertheless a comparison between different DIP methods is possible.Four different algorithms were developed using The MathWorks MatLab, Massachusetts[3] in order to automatically measure the crack length and a comparison of the obtained resultsis made.Algorithm A is based on thresholding [4] each image of the sequence in order to detect thewhite painted region around the crack. In algorithm B, the image sequence is processed by afilter which reinforces horizontal lines such as the crack, and then isolated pixels are removedfrom the images using morphological cleaning [4]. In algorithm C, the first of two consecutiveimages is subtracted from the second one in order to detect the crack as a difference betweenboth images. Algorithm D is based on the optical flow concept developed by Horn [5]. Thebasic idea is to determine the velocity of each pixel in the image when this changes its positionfrom one image to the next in the analyzed sequence, and relate this information to the growing crack

Remote Analysis of Grain Size Characteristic in Submarine Pyroclastic Deposits from Kolumbo Volcano, Greece

Grain size characteristics of pyroclastic deposits provide valuable information about source eruption energetics and depositional processes. Maximum size and sorting are often used to discriminate between fallout and sediment gravity flow processes during explosive eruptions. In the submarine environment the collection of such data in thick pyroclastic sequences is extremely challenging and potentially time consuming. A method has been developed to extract grain size information from stereo images collected by a remotely operated vehicle (ROV). In the summer of 2010 the ROV Hercules collected a suite of stereo images from a thick pumice sequence in the caldera walls of Kolumbo submarine volcano located about seven kilometers off the coast of Santorini, Greece. The highly stratified, pumice-rich deposit was likely created by the last explosive eruption of the volcano that took place in 1650 AD. Each image was taken from a distance of only a few meters from the outcrop in order to capture the outlines of individual clasts with relatively high resolution. Mosaics of individual images taken as the ROV transected approximately 150 meters of vertical outcrop were used to create large-scale vertical stratigraphic columns that proved useful for overall documentation of the eruption sequence and intracaldera correlations of distinct tephra units. Initial image processing techniques, including morphological operations, edge detection, shape and size estimation were implemented in MatLab and applied to a subset of individual images of the mosiacs. A large variety of algorithms were tested in order to best discriminate the outlines of individual pumices. This proved to be challenging owing to the close packing and overlapping of individual pumices. Preliminary success was achieved in discriminating the outlines of the large particles and measurements were carried out on the largest clasts present at different stratigraphic levels. In addition, semi-quantitative analysis of the size distribution could also be determined for individual images. Although a complete size distribution is not possible with this technique, information about the relative distribution of large and medium size clasts is likely to provide a reasonable proxy for the overall sorting of submarine deposits. Our preliminary work represents the first attempt to carry out an in situ granulometric analysis of a thick submarine pyroclastic sequence. This general technique is likely to be valuable in future studies of submarine explosive volcanism given the recent discoveries of extensive pumiceous deposits in many submarine calderas associated with subduction zone environments. AGU session number OS13A-150

Extraction of text regions in natural images

The detection and extraction of text regions in an image is a well known problem in the computer vision research area. The goal of this project is to compare two basic approaches to text extraction in natural (non-document) images: edge-based and connected-component based. The algorithms are implemented and evaluated using a set of images of natural scenes that vary along the dimensions of lighting, scale and orientation. Accuracy, precision and recall rates for each approach are analyzed to determine the success and limitations of each approach. Recommendations for improvements are given based on the results

Pupil Detection Based on Color Difference and Circular Hough Transfor

Human pupil eye detection is a significant stage in iris segmentation which is representing one of the most important steps in iris recognition. In this paper, we present a new method of highly accurate pupil detection. This method is consisting of many steps to detect the boundary of the pupil. First, the read eye image (R, G, B), then determine the work area which is consist of many steps to detect the boundary of the pupil. The determination of the work area contains many circles which are larger than pupil region. The work area is necessary to determine pupil region and neighborhood regions afterward the difference in color and intensity between pupil region and surrounding area is utilized, where the pupil region has color and intensity less than surrounding area. After the process of detecting pupil region many steps on the resulting image is applied in order to concentrate the pupil region and delete the others regions by using many methods such as dilation, erosion, canny filter, circle hough transforms to detect pupil region as well as apply optimization to choose the best circle that represents the pupil area. The proposed method is applied for images from palacky university, it achieves to 100 % accura

Automated streak-seeding with micromachined silicon tools

This report presents a new approach to streak-seeding based on custom-designed silicon microtools. Experimental data show that the microtools produce similar results to the commonly used boar bristles. One advantage to using silicon is that it is rigid and can easily serve as an accurately calibrated end-effector on a micro-robotic system. Additionally, the fabrication technology allows the production of microtools of various shapes and sizes. A working prototype of an automatic streak-seeding system based on these microtools was built and successfully applied for protein crystallization