Design and Analysis of Segmentation and Region Growing Method of Diagnosing Eye Images (original) (raw)
Related papers
Automatic Segmentation of Retinal Images by Using Morphological Watershed and Region Growing Method
Retinal image segmentation is essential for diagnosing various problems occurs in eye. Retinal image segment is one of the critical issues because these images contain very small nerves and some artifacts present in it. This paper proposes an automatic morphological watershed segmentation and region growing method to change the representation of an image into something that is more meaningful and easier to analyze the interested object. There are several methods that intend to perform segmentation, but it is difficult to adapt easily and detect the very small nerves accurately. To resolve this problem, this paper aims to present an adaptable automatic morphological watershed segmentation and region growing method that can be applied to any type of retinal images which is exactly diagnosed even with the small changes that occur in the image. This proposed method is based in a model of morph function which applies the morphological watershed operator to a gray scale image. Morphological segment technique is used to segment the image and selecting the specific image objects, thinning the object to found the root nerves. After using a morphological watershed operation to expose the basic elements within an image, it is often useful to extract and analyze specific information about those image elements. The region growing segmentation performs region growing for a given image region within the array that are connected to neighboring region pixels and that fall within provided constraints[7].
Detection of Optic Cup Disc using Morphological Pixel Classification Technique.
International Journal of Engineering Sciences & Research Technology, 2014
Retinal image segmentation is important for diagnosing various issues happens in eye. Retinal image segment is one among the crucial problems as a result of this image contains terribly little nerves and a few artifacts present in it. This paper proposes associate automatic morphological segmentation technique to vary the illustration of a picture into one thing that's additional significant and easier to research the interested object. There are many ways that shall perform segmentation, however it's tough to adapt simply and observe the terribly little nerves accurately. To resolve this drawback, this paper associate flexible automatic morphological segmentation and region growing technique that may be applied to any form of retinal pictures that is strictly diagnosed even with the little changes that occur within the image. This projected technique is predicated in a very model of morph perform that applies the morphological watershed operator to a grey scale image. Morphological phase technique is employed to phase the image and choosing the particular image objects, cutting the article to found the basis nerves. Once employing a morphological operation to show the fundamental components among a picture, it's typically helpful to extract and analyze specific info regarding those image components. This proposed segmentation performs region growing for a given image region among the array that are connected to neighboring region pixels which fall among provided constraints.
Morphological Techniques for Medical Images: A Review
Image processing is playing a very important role in medical imaging with its versatile applications and features towards the development of computer aided diagnostic systems, automatic detections of abnormalities and enhancement in ultrasonic, computed tomography, magnetic resonance images and lots more applications. Medical images morphology is a field of study where the medical images are observed and processed on basis of geometrical and changing structures. Medical images morphological techniques has been reviewed in this study underlying the some human organ images, the associated diseases and processing techniques to address some anatomical problem detection. Images of Human brain, bone, heart, carotid, iris, lesion, liver and lung have been discussed in this study.
A new morphological segmentation algorithm for biomedical imaging applications
Image Processing: Machine Vision Applications II, 2009
Images of high geometrical complexity are found in various applications in the fields of image processing and computer vision. Medical imaging is such an application, where the processing of digitized images reveals vital information for the therapeutic or diagnostic algorithms. However, the segmentation of these images has been proved to be one of the most challenging topics in modern computer vision algorithms. The light interaction with tissues and the geometrical complexity with the tangent objects are among the most common reasons that many segmentation techniques nowadays are strictly related to specific applications and image acquisition protocols. In this paper a sophisticated segmentation algorithm is introduced that succeeds into overcoming the application dependent accuracy levels. This algorithm is based on morphological sequential filtering, combined with a watershed transformation. The results on various biomedical test images present increased accuracy, which is independent of the image acquisition protocol. This method can provide researchers with a valuable tool, which makes the classification or the follow-up faster, more accurate and objective.
Medical Imaging 2003: Image Processing, 2003
Age-Related Macular Degeneration (ARMD) is the leading cause of irreversible visual loss among the elderly in the US and Europe. A computer-based system has been developed to provide the ability to track the position and margin of the ARMD associated lesion; drusen. Variations in the subject's retinal pigmentation, size and profusion of the lesions, and differences in image illumination and quality present significant challenges to most segmentation algorithms. An algorithm is presented that first classifies the image to optimize the variables of a mathematical morphology algorithm. A binary image is found by applying Otsu's method to the reconstructed image. Lesion size and area distribution statistics are then calculated. For training and validation, the University of Wisconsin provided longitudinal images of 22 subjects from their 10 year Beaver Dam Study. Using the Wisconsin Age-Related Maculopathy Grading System, three graders classified the retinal images according to drusen size and area of involvement. The percentages within the acceptable error between the three graders and the computer are as follows: Grader-A: Area: 84% Size: 81%; Grader-B: Area: 63% Size: 76%; Grader-C: Area: 81% Size: 88%. To validate the segmented position and boundary one grader was asked to digitally outline the drusen boundary. The average accuracy based on sensitivity and specificity was 0.87 for thirty four marked regions.
Analysis of image Segmentation techniques for medical images
Image segmentation is an image processing technique that divides an image into contiguous regions or segments and typically used to locate objects and boundaries such as lines, curves, etc. in images. Regions are disjoint with some property for each region such as pixel intensity, grey level texture, or colour, etc., because a single point cannot be contained in two different regions. Image Segmentation techniques enable the design of automated segmentation techniques. Several such algorithms are proposed in the literature to simplify and/or change the representation of an image into something that is more meaningful and easier to analyse. In the proposed system, segmentation of images using Region Growing algorithm (based on seed pixels) is done in more than one processor. This paper explores several such algorithms which have been proved to perform on multi-categories of images specifically in medical images. This review paper also suggests interesting directions for further research.
Segmentation of medical images using adaptive region growing
Medical Imaging 2001: Image Processing, 2001
Interaction increases flexibility of segmentation but it leads to undesirable behavior of an algorithm if knowledge being requested is inappropriate. In region growing, this is the case for defining the homogeneity criterion, as its specification depends also on image formation properties that are not known to the user. We developed a region growing algorithm that learns its homogeneity criterion automatically from characteristics of the region to be segmented. The method is based on a model that describes homogeneity and simple shape properties of the region. Parameters of the homogeneity criterion are estimated from sample locations in the region. These locations are selected sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously. This approach was extended to a fully automatic and complete segmentation method by using the pixels with the smallest gradient length in the not yet segmented image region as a seed point. The methods were tested for segmentation on test images and of structures in CT and MR images. We found the methods to work reliable if the model assumption on homogeneity and region characteristics are true. Furthermore, the model is simple but robust, thus allowing for a certain degree of deviation from model constraints and still delivering the expected segmentation result.
Automatic Detection of Landmarks and Abnormalities in Eye Fundus Images
The eye fundus is the only organ of the central nervous system that can be photographed directly, as it can be seen through the eye's pupil. Thereby, computer vision systems are developed with the aim of localizing and analyzing the eye fundus landmarks, namely the optic disc, blood vessels, and macula. Particularly, the optic disc segmentation is a key element in screening systems which facilitates the detection of lesions that affect the interior surface of the eye (i.e. fundus), such as glaucoma and diabetic retinopathy.
An Efficient Segmentation Technique for Different Medical Image Modalities
Menoufia Journal of Electronic Engineering Research, 2020
In this paper a study on the segmentation of the medical image is carried out. Image segmentation is the process of splitting an image into a number of non-overlapped segments (sets of pixels, also known as image objects). The success of image analysis process depends on accuracy of segmentation process, but a successful segmentation of an image is generally a difficult problem. During an image preprocessing operation, the input given is an image and its output is an enhanced high-quality image as per the techniques used. This paper provides a solid introduction to image enhancement along with image segmentation technique fundamentals. Firstly, the local spatial information of the image is combined with fuzzy c-mean by introducing morphological reconstruction operation to ensure noise-immunity and image detail-protection. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, the modification of membership partition depends on...
Physica Medica, 2018
Iris neoplasm is a non-symptom cancer that causes a gradual loss of sight. The first purpose of this study was to present a novel and automatic method for segmenting the iris tumors and detecting the corresponding areas changing along time. The second aim of this work was to investigate several recently published methods after being applied for the iris tumors segmentation. Methods: Our approach consists firstly in segmenting the iris region by using the Vander Lugt correlator based active contour method. Secondly, by treating only the iris region, a K-means clustering model was used to assign the tumorous tissue to one pixel-cluster. This model is quite sensitive to the center initialization and to the choice of the distance measure. To solve these problems, a proportional probability based approach was introduced for the cluster center initialization, and the impact of several distance measure was investigated. The proposed method and the different comparative methods were evaluated on two databases: the Eye Cancer and the Miles Research. Results: Results reported using several performance metrics reveal that the first step assures the detection of all iris tumors with an accuracy of 100%. Additionally, the proposed method yields better performance compared to the recently published methods.