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

Detection of retinal blood vessels from ophthalmoscope images using morphological approach

Accurate segmentation of retinal blood vessels is an essential task for diagnosis of various pathological disorders. In this paper, a novel method has been introduced for segmenting retinal blood vessels which involves pre-processing, segmentation and post-processing. The pre-processing stage enhanced the image using contrast limited adaptive histogram equalization and 2D Gabor wavelet. The enhanced image is segmented using geodesic operators and a final segmentation output is obtained by applying a post-processing stage that involves hole filling and removal of isolated pixels. The performance of the proposed method is evaluated on the publicly available Digital retinal images for vessel extraction (DRIVE) and High-resolution fundus (HRF) databases using five different measurements and experimental analysis shows that the proposed method reach an average accuracy of 0.9541 on DRIVE database and 0.9568, 0.9478 and 0.9613 on HRF database with healthy, diabetic retinopathy (DR) and glaucomatous images respectively

Object Detection and Tracking using Watershed Segmentation and KLT Tracker

In this paper a moving object is extracted from a video using video object detection algorithm based on spatial and temporal segmentation The technique begins with temporal segmentation in which edge map is extracted using edge operator The initial binary mask is obtained by using morphological operation applied on initial edge map The next phase is spatial segmentation where gradient image is obtained by multi-scale morphological operator The modified gradient image is obtained by the operator applied over the current frame At last moving object is extracted by precisely and accurately by watershed segmentation which is performed on the modified gradient image Again morphological operation is applied on the output to get final binary mask This binary mask is then complemented to yield the contour line of the video object Using the binary mask the video object is extracted from the video frames After detection of video object the object tracking is performed using Kanade Lucas Tomasi KLT feature tracke

Development of Some Novel Spatial-Domain and Transform-Domain Digital Image Filters

Some spatial-domain and transform-domain digital image filtering algorithms have been developed in this thesis to suppress additive white Gaussian noise (AWGN). In many occasions, noise in digital images is found to be additive in nature with uniform power in the whole bandwidth and with Gaussian probability distribution. Such a noise is referred to as Additive White Gaussian Noise (AWGN). It is difficult to suppress AWGN since it corrupts almost all pixels in an image. The arithmetic mean filter, commonly known as Mean filter, can be employed to suppress AWGN but it introduces a blurring effect. Image denoising is usually required to be performed before display or further processing like segmentation, feature extraction, object recognition, texture analysis, etc. The purpose of denoising is to suppress the noise quite efficiently while retaining the edges and other detailed features as much as possible

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.    

Wavelet based transition region extraction for image segmentation

Transition region based approaches are recent hybrid segmentation techniques well known for its simplicity and effectiveness. Here, the segmentation effectiveness depends on robust extraction of transition regions. So, we have proposed a transition region method which initially decomposes the gray image in wavelet domain. Two existing transition region approaches are applied on approximate coefficients to extract transition region feature matrix. Using this feature matrix the corresponding prominent wavelet coefficients of different bands are found. Inverse wavelet transform are then applied on the modified coefficients to get edge image with more than one pixel width. Otsu thresholding is applied on it to get transition regions. Further, morphological operations are applied to extract the object regions. Finally, the objects are extracted using the object regions. The wavelet domain approach extracts robust transition regions resulting in better segmentation. The proposed method is compared with different existing image segmentation methods. Experimental results reveal that the proposed method achieve 0.95 overall segmentation accuracy. It also works well for extraction of single as well as multiple objects from images

Feature based transition region extraction for image segmentation: Application to worm separation from leaves

Transition region based approaches are recent hybrid segmentation techniques well known for its simplicity and effectiveness. Here, the segmentation effectiveness depends on robust extraction of transition regions. So, we have proposed transition region extraction method for image segmentation. The proposed method initially decomposes the gray image in wavelet domain. Local standard deviation filtering and thresholding operation is used to extract transition region feature matrix. Using this feature matrix, the corresponding prominent wavelet coefficients of different bands are found. The inverse wavelet transform is then applied to the modified coefficients to get edge image with more than one-pixel width. Global thresholding is applied to get transition regions. Further, it undergoes morphological thinning and region filling operation to extract the object regions. Finally, the objects are extracted using the object regions. The proposed method is compared with different image segmentation methods. An experimental result reveals that the proposed method outperforms other methods for segmentation of images containing single and multiple objects. The proposed method can also be applied for worm separation from leaves

Transition region based single and multiple object segmentation of gray scale images

Transition region based image segmentation has proved to be the simple and effective image segmentation technique. However, the methods have two shortcomings. First, they are applied mostly for image segmentation containing a single object. Second, the methods are effective only when the images contain simple background and foreground. The performance deteriorates when background and foreground are textured or of varying intensities. To overcome this, a novel method has been proposed for multi-object segmentation. In this method, a global threshold and the local variance is computed to achieve the transition regions. The transition regions thus obtained undergo morphological operations to get the object contours. The morphological filling operation is employed on object contours to extract object regions. Finally, the objects are extracted from the image from these object regions. The proposed method is compared with different methods for single-object segmentation, and experimental results show superior performance. The method also works efficiently for multiple object segmentation