04-10-2016, 02:34 PM
AUTOMATED VESSEL SEGMENTATION USING INFINITE PERIMETER ACTIVE CONTOUR MODEL WITH HYBRID REGION INFORMATION WITH APPLICATION TO RETINAL IMAGES
1457686922-new.word.docx (Size: 581.7 KB / Downloads: 6)
INTRODUCTION:
Blood vessels can be conceptualized anatomically as an intricate network, or tree-like structure (or vasculature), of hollow tubes of different sizes and compositions including arteries, arterioles, capillaries, venules, and veins. Their continuing integrity is vital to nurture life: any damage to them could lead to profound complications, including stroke, diabetes, arteriosclerosis, cardiovascular diseases and hypertension, to name only the most obvious. Vascular diseases are often life-critical for individuals, and present a challenging public health problem for society. The drive for better understanding and management of these conditions naturally motivates the need for improved imaging techniques. The detection and analysis of the vessels in medical images is a fundamental task in many clinical applications to support early detection, diagnosis and optimal treatment. In line with the proliferation of imaging modalities, there is an ever-increasing demand for automated vessel analysis systems for which where blood vessel segmentation is the first and most important step. As blood vessels can be seen as linear structures distributed at different orientations and scales in an image, various kernels (or enhancement filters) have been proposed to enhance them in order to ease the segmentation problem. In particular, a local phase based filter recently introduced by Lathen et al seems to be superior to intensity based filters as it is immune to intensity inhomogeneity and is capable of faithfully enhancing vessels of different widths.
It is worth noting that morphological filters such as path opening in combination with multiscale Gaussian filters.The main disadvantage of morphological methods is that they do not consider the known vessel cross-sectional shape information, and the use of an overly long structuring element may cause difficulty in detecting highly tortuous vessels.
SCOPE OF THE PROJECT:
We propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularize, provided by using L2 Lebesgue measure of the -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature’s boundaries (i.e. H1 Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map.
LITERATURE SURVEY:
1. “Detection and Measurement of Retinal Vessels in Fundus Images Using Amplitude Modified Second-Order Gaussian Filter” by Luo Gang, Opas Chutatape, and Shankar M. Krishnan
In this paper, the fitness of estimating vessel profiles with Gaussian function is evaluated and an amplitude-modified second-order Gaussian filter is proposed for the detection and measurement of vessels. Mathematical analysis is given and supported by a simulation and experiments to demonstrate that the vessel width can be measured in linear relationship with the “spreading factor” of the matched filter when the magnitude coefficient of the filter is suitably assigned. The absolute value of vessel diameter can be determined simply by using a precalibrated line, which is typically required since images are always system dependent. The experiment shows that the inclusion of the width measurement in the detection process can improve the performance of matched filter and result in a significant increase in success rate of detection.
2. “Improved Detection of the Central Reflex in Retinal Vessels Using a Generalized Dual-Gaussian Model and Robust” by Harihar Narasimha Iyer, Vijay Mahadevan, James M. Beach, and Badrinath Roysam
This updates an earlier publication by the authors describing a robust framework for detecting vasculature in noisy retinal fundus images. We improved the handling of the “central reflex” phenomenon in which a vessel has a “hollow” appearance. This is particularly pronounced in dual-wavelength images acquired at 570 and 600 nm for retinal oximetry. It is prominent in the 600 nm images that are sensitive to the blood oxygen content. Improved segmentation of these vessels is needed to improve oximetry. We show that the use of a generalized dual-Gaussian model for the vessel intensity profile instead of the Gaussian yields a significant improvement. Our method can account for variations in the strength of the central reflex, the relative contrast, width, orientation, scale, and imaging noise. It also enables the classification of regular and central reflex vessels. The proposed method yielded a sensitivity of 72% compared to 38% by the algorithm of Can et al., and 60% by the robust detection based on a single-Gaussian model. The specificity for the methods were 95%, 97%,
and 98%, respectively.
3. “Parallel Multiscale Feature Extraction and Region Growing: Application in Retinal Blood Vessel Detection” by Miguel A. Palomera-P´erez, M. Elena Martinez-Perez, Hector Ben´ıtez-P´erez, and Jorge Luis Ortega-Arjona
This paper presents a parallel implementation based on insight segmentation and registration toolkit for a multiscale feature extraction and region growing algorithm, applied to retinal blood vessels segmentation. This implementation is capable of achieving an accuracy (Ac) comparable to its serial counterpart (about 92%), but 8 to 10 times faster. In this paper, the Ac of this parallel implementation is evaluated by comparison with expert
manual segmentation (obtained from public databases). On the other hand, its performance is compared with previous published serial implementations. Both these characteristics make this parallel implementation feasible for the analysis of a larger amount of high-resolution retinal images, achieving a faster and high-quality segmentation of retinal blood vessels.
4. Retinal Vessel Segmentation Using the 2-D Gabor Wavelet and Supervised Classification by João V. B. Soares*, Jorge J. G. Leandro, Roberto M. Cesar Jr., Herbert F. Jelinek, and Michael J. Cree
We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel’s feature vector. Feature vectors are composed of the pixel’s intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method’s performance is evaluated on publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et al., 2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.
5. An Active Contour Model for Segmenting and Measuring Retinal Vessels by Bashir Al-Diri, Andrew Hunter, and David Steel
This paper presents an algorithm for segmenting and measuring retinal vessels, by growing a “Ribbon of Twins” active contour model, which uses two pairs of contours to capture each vessel edge, while maintaining width consistency. The algorithm is initialized using a generalized morphological order filter to identify approximate vessels centerlines. Once the vessel segments are identified the network topology is determined using an implicit neural cost function to resolve junction configurations. The algorithm is robust, and can accurately locate vessel edges under difficult conditions, including noisy blurred edges, closely parallel vessels, light reflex phenomena, and very fine vessels. It yields precise vessel width measurements, with subpixel average width errors.We compare the algorithm with several benchmarks from the literature, demonstrating higher segmentation sensitivity and more accurate width measurement.
FUNCTIONAL REQUIREMENTS
A functional requirement defines a function of a software-system or its component. A function is described as a set of inputs, the behavior, and outputs. Our system requires minimum three systems to achieve this concept.
NON-FUNCTIONAL REQUIREMENTS
EFFICIENCY
Our application efficiently characterizes the server and the cluster requests and response.
MODULES:
1. TYPICAL VESSEL ENHANCEMENT FILTER
2. ACTIVE CONTOUR MODEL
3. IPACHI
SOFTWARE REQUIREMENT:
MATLAB 7.14 Version R2012
MATLAB
The MATLAB high-performance language for technical computing integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.
Data Exploration ,Acquisition ,Analyzing &Visualization
Engineering drawing and Scientific graphics
Analyzing of algorithmic designing and development
Mathematical functions and Computational functions
Simulating problems prototyping and modeling
Application development programming using GUI building environment.
Using MATLAB, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and FORTRAN.
ADVANTAGE:
• Different types of region information, such as the combination of intensity information and local phase based enhancement map.
• Analysis, such as measurements of diameters and tortuosity of the vessels, classification of veins and arteries, calculation of the arteriovenous ratio.
• Automated or semi-automated segmentation methods would have improvements in efficiency and accuracy.
• Fast, readily available, highest spatial resolution.
APPLICATION:
• The model will be applicable to the management of other eye conditions such as corneal neovascularization.
• To support early detection, diagnosis and optimal treatment.
• In line with the proliferation of imaging modalities, there is an ever-increasing demand for automated vessel analysis systems for which where blood vessel segmentation is the first and most important step.
• Image segmentation plays an essential role in many medical applications.
• Low SNR conditions and various artifacts makes its automation challenging.
• To achieve robust and accurate segmentation
CONCLUSION:
In this paper, we have proposed a new infinite perimeter active contour model with hybrid region terms for the vessel segmentation problem. This model has been applied to three publicly available retinal datasets and the results demonstrate that it outperforms most of the existing methods in terms of segmentation accuracy. Vessel segmentation still remains a challenging medical image analysis problem despite considerable effort in research. Many factors come together to make this problem difficult to be addressed. The images under consideration often come with noise and blur, and suffer from uneven illumination (or biased field in magnetic resonance imaging [MRI]) problems. In addition, although vessels in an image are similar to each other in general, they have different widths and orientations and sometimes different appearances in terms of intensity, color or local shape, which may become more complicated when disease is present.