Ophthalmologic Image Registration Based on Shape-Context: Application to Fundus Autofluorescence (FAF) Images

Online access to subscriber only at http://www.actapress.com/Content_Of_Proceeding.aspx?ProceedingID=494International audienceA novel registration algorithm, which was developed in order to facilitate ophthalmologic image processing, is presented in this paper. It has been evaluated on FAF images, which present low Si gnal/Noise Ratio (SNR) and variations in dynamic grayscale range. These characteristics complicate the registration process and cause a failure to area-based registration techniques [1, 2] . Our method is based on shape-context theory [3] . In the first step, images are enhanced by Gaussian model based histog ram modification. Features are extracted in the next step by morphological operators, which are used to detect an approximation of vascular tree from both reference and floating images. Simplified medial axis of vessels is then calculated. From each image, a set of control points called Bifurcation Points (BPs) is extracted from the medial axis through a new fast algorithm. Radial histogram is formed for each BP using the medial axis. The Chi2 distance is measured between two sets of BPs based on radial histogram. Hungarian algorithm is applied to assign the correspondence among BPs from reference and floating images. The algorithmic robustness is evaluated by mutual information criteria between manual registration considered as Ground Truth and automatic one

RETINAL VESSEL DETECTION USING SELF-MATCHED FILTERING

ABSTRACT Automated analysis of retinal images usually requires estimating the positions of blood vessels, which contain important features for image alignment and abnormality detection. Matched filtering can produce the best results but is difficult to implement because the vessel orientations and widths are unknown beforehand. Many researchers use Hessian filtering, which provides an estimate for vessel orientation through the use of three orientation templates. We propose a novel filtering approach, called self-matched filtering, which is based on the 180 • rotated version of the noisy vessel signal in the local neighborhood. We show that even though the proposed filter achieves half the signal-to-noise ratio of a matched filter, it does not require the estimation of the vessel scale and orientation, and can outperform Hessian filtering by up to a factor of two in terms of miss detection error

Retinal Image Registration and Comparison for Clinical Decision Support

Background For eye diseases, such as glaucoma and age-related macular degeneration (ARMD), involved in long-term degeneration procedure, longitudinal comparison of retinal images is a common step for reliable diagnosis of these kinds of diseases. Aims To provide a retinal image registration approach for longitudinal retinal image alignment and comparison. Method Two image registration solutions were proposed for facing different image qualities of retinal images to make the registration methods more robust and feasible in a clinical application system. Results Thirty pairs of longitudinal retinal images were used for the registration test. The experiments showed both solutions provided good performance for the accurate image registrations with efficiency. Conclusion We proposed a set of retinal image registration solutions for longitudinal retinal image observation and comparison targeting a clinical application environment

Incorporating spatial information for microaneurysm detection in retinal images

The presence of microaneurysms(MAs) in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR). This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method

Image Registration - Application in ophthalmology and ultrasonography

Registrace medicínských obrazů je v dnešních dnech široce používaná, ale zároveň je i jednou z oblastí zájmu vědeckého výzkumu. Stále nové a vylepšené zobrazovací systémy si žádají stále lepší a výkonnější metody registrace obrazu. Takovou oblastí je i kontrastní ultrazvukové zobrazování. Díky časové proměnlivému kontrastu v obraze, nízkému poměru signál/šum a specifickému šumu typu spekle je registrace ultrazvukových obrazu velice náročná. Dalším problémem je hodnocení kvality registrace. V této dizertační práci je představena metoda registrace ultrazvukových kontrastních sekvencí založena na automatické fragmentaci sekvence do podsekvencí. Následně jsou registrovány obrazy s podobnými vlastnostmi. Dále je představena nová metoda pro hodnocení kvality registrace na základě porovnání perfuzních modelů. Metoda registrace i hodnocení byla testována jak na datech získaných za pomocí fantomu, tak i na reálných pacientských datech. Výsledky pak byly porovnány se standardními metodami publikovanými v odborných článcích. Druhá menší část práce je tvořena ukázkami aplikací různých registračních metod v oftalmologii a návrhy na jejich zlepšení. Jedná se o oblast zobrazovacích systému, kde se registračních metod široce využívá. Kromě jasových registračních metod zde nachází velké uplatnění metody registrace založené na detekci významných bodů. Představené registrační přístupy tak směřují především k detekci těchto významných bodů a stanovení jejich vzájemných korespondencí v jednotlivých obrazech.Image registration is widely used in clinical practice. However image registration and its~evaluation is still challenging especially with regards to new possibilities of various modalities. One of these areas is contrast-enhanced ultrasound imaging. The time-dependent image contrast, low signal-to-noise ratio and specific speckle pattern make preprocessing and image registration difficult. In this thesis a method for registration of images in ultrasound contrast-enhanced sequences is proposed. The method is based on automatic fragmentation into image subsequences in which the images with similar characteristics are registered. The new evaluation method based on comparison of perfusion model is proposed. Registration and evaluation method was tested on a flow phantom and real patient data and compared with a standard methods proposed i literature. The second part of this thesis contains examples of application of image registration in~ophthalmology and proposition for its improvement. In this area the image registration methods are widely used, especially landmark based image registration method. In this thesis methods for landmark detection and its correspondence estimation are proposed.

망막 질환 진단을 위한 효과적인 다중 영상 정합 기법

학위논문 (석사)-- 서울대학교 대학원 : 전기공학부, 2013. 8. 서종모.안저 사진은 망막질환을 진단하는데 매우 유용하게 사용된다. 그런데 안저 촬영시 동공을 통해 찍어야 하므로 한 장에 담을 수 있는 망막의 범위가 한정되어 있다. 따라서, 전체 망막을 관찰하려면 다양한 각도에서 안저 사진을 찍은 후 하나의 사진으로 합쳐야 한다. 상용화된 망막 사진 정합 프로그램이 있지만 결과물에 대한 품질이 아직은 만족스럽지 못하다. 이 연구에서는 기존의 망막 사진 정합 알고리즘을 개선하여 보다 고품질의 전체 망막 영상을 얻을 수 있는 방안을 연구하였다. 이 연구에서는 특징점 추출 방식(Harris corner detector, multi-scale Lapacian of Gaussian, vessel bifurcation detection)과 특징점 비교방식(descriptor matching, template matching)을 안저 사진에 적용하여 각각의 성능을 평가하였다. 그리고 추가로 혈관 영상을 이용한 대응점 추출 방식을 고안하였다. 이것들을 이용해 효과적으로 대응점을 찾을 수 있는 알고리즘을 개발하였다. 이차 변형모델의 문제점을 해결하기 위해 실제 안구를 바탕으로 하는 안구 모델과 안저 사진과의 관계를 3차원 공간에서 가상적으로 구현함으로써 안구의 곡률을 예측하고 이를 이용해 이미지의 비 이상적인 변형을 막는 방식을 고안하였다. 그 후 안저 사진의 형태를 고려하여 외곽선에 의해 생기는 이질감을 없애는 방법을 만들었다. 프로그램 인터페이스를 매우 단순하고 직관적으로 구현함으로써 사용자의 편의성을 증진 시켰으며 추가 연구를 통해 성능을 좀더 개선하면 망막질환 진단에 널리 사용될 수 있을 것으로 기대한다.제 1 장 기존 연구 1 1.1 연구 배경 1 1.2 영상 정합 방법 3 1.3 영상 정합 알고리즘 5 1.4 기존 연구의 한계점과 앞으로 제안할 방식 10 제 2 장 영상 분석 알고리즘 11 2.1 특징점 찾기 11 2.2 대응점 찾기 14 2.3 새롭게 고안한 분기점 비교 알고리즘 15 2.4 통 계 19 2.5 통합 특징점 추출 방식 21 제 3 장 영상 변환 알고리즘 22 3.1 이차 변형 행렬 추정과 Linear Joint Solution 22 3.2 영상의 비이상적 변형 제한 모델 23 제 4 장 실험 결과 27 제 5 장 분석 및 토의 30 제 6 장 참고문헌 32Maste

Processing multiple image streams for real-time monitoring of parking lots

We present a system to detect parked vehicles in a typical parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. Performance enhancements are made on the algorithm so that it operates well in the context of multiple image streams. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.<br /

Superimposition of eye fundus images for longitudinal analysis from large public health databases

In this paper, a method is presented for superimposition (i.e. registration) of eye fundus images from persons with diabetes screened over many years for diabetic retinopathy. The method is fully automatic and robust to camera changes and colour variations across the images both in space and time. All the stages of the process are designed for longitudinal analysis of cohort public health databases where retinal examinations are made at approximately yearly intervals. The method relies on a model correcting two radial distortions and an affine transformation between pairs of images which is robustly fitted on salient points. Each stage involves linear estimators followed by non-linear optimisation. The model of image warping is also invertible for fast computation. The method has been validated (1) on a simulated montage and (2) on public health databases with 69 patients with high quality images (271 pairs acquired mostly with different types of camera and 268 pairs acquired mostly with the same type of camera) with success rates of 92% and 98%, and five patients (20 pairs) with low quality images with a success rate of 100%. Compared to two state-of-the-art methods, ours gives better results.Comment: This is an author-created, un-copyedited version of an article published in Biomedical Physics \& Engineering Express. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/2057-1976/aa7d1

Feature-Based Image Registration

Image registration is the fundamental task used to match two or more partially overlapping images taken, for example, at different times, from different sensors, or from different viewpoints and stitch these images into one panoramic image comprising the whole scene. It is a fundamental image processing technique and is very useful in integrating information from different sensors, finding changes in images taken at different times, inferring three-dimensional information from stereo images, and recognizing model-based objects. Some techniques are proposed to find a geometrical transformation that relates the points of an image to their corresponding points of another image. To register two images, the coordinate transformation between a pair of images must be found. In this thesis, a feature-based method is developed to efficiently estimate an eight-parametric projective transformation model between pairs of images. The proposed approach applies wavelet transform to extract a number of feature points as the basis for registration. Each selected feature point is an edge point whose edge response is the maximum within a neighborhood. During the real matching process, we check each candidate pair in advance to see if it can possibly become a correct matching pair. Due to this checking, many unnecessary calculations involving cross-correlations can be screened in advance. Therefore, the search time for obtaining correct matching pairs is reduced significantly. Finally, based on the set of correctly matched feature point pairs, the transformation between two partially overlapping images can be decided