The Impact of Different Image Thresholding based Mammogram Image Segmentation- A Review

Images are examined and discretized numerical capacities. The goal of computerized image processing is to enhance the nature of pictorial data and to encourage programmed machine elucidation. A computerized imaging framework ought to have fundamental segments for picture procurement, exceptional equipment for encouraging picture applications, and a tremendous measure of memory for capacity and info/yield gadgets. Picture segmentation is the field broadly scrutinized particularly in numerous restorative applications and still offers different difficulties for the specialists. Segmentation is a critical errand to recognize districts suspicious of tumor in computerized mammograms. Every last picture have distinctive sorts of edges and diverse levels of limits. In picture transforming, the most regularly utilized strategy as a part of extricating articles from a picture is "thresholding". Thresholding is a prevalent device for picture segmentation for its straightforwardness, particularly in the fields where ongoing handling is required

A Hierarchical Image Processing Approach for Diagnostic Analysis of Microcirculation Videos

Knowledge of the microcirculatory system has added significant value to the analysis of tissue oxygenation and perfusion. While developments in videomicroscopy technology have enabled medical researchers and physicians to observe the microvascular system, the available software tools are limited in their capabilities to determine quantitative features of microcirculation, either automatically or accurately. In particular, microvessel density has been a critical diagnostic measure in evaluating disease progression and a prognostic indicator in various clinical conditions. As a result, automated analysis of the microcirculatory system can be substantially beneficial in various real-time and off-line therapeutic medical applications, such as optimization of resuscitation. This study focuses on the development of an algorithm to automatically segment microvessels, calculate the density of capillaries in microcirculatory videos, and determine the distribution of blood circulation. The proposed technique is divided into four major steps: video stabilization, video enhancement, segmentation and post-processing. The stabilization step estimates motion and corrects for the motion artifacts using an appropriate motion model. Video enhancement improves the visual quality of video frames through preprocessing, vessel enhancement and edge enhancement. The resulting frames are combined through an adjusted weighted median filter and the resulting frame is then thresholded using an entropic thresholding technique. Finally, a region growing technique is utilized to correct for the discontinuity of blood vessels. Using the final binary results, the most commonly used measure for the assessment of microcirculation, i.e. Functional Capillary Density (FCD), is calculated. The designed technique is applied to video recordings of healthy and diseased human and animal samples obtained by MicroScan device based on Sidestream Dark Field (SDF) imaging modality. To validate the final results, the calculated FCD results are compared with the results obtained by blind detailed inspection of three medical experts, who have used AVA (Automated Vascular Analysis) semi-automated microcirculation analysis software. Since there is neither a fully automated accurate microcirculation analysis program, nor a publicly available annotated database of microcirculation videos, the results acquired by the experts are considered the gold standard. Bland-Altman plots show that there is ``Good Agreement between the results of the algorithm and that of gold standard. In summary, the main objective of this study is to eliminate the need for human interaction to edit/ correct results, to improve the accuracy of stabilization and segmentation, and to reduce the overall computation time. The proposed methodology impacts the field of computer science through development of image processing techniques to discover the knowledge in grayscale video frames. The broad impact of this work is to assist physicians, medical researchers and caregivers in making diagnostic and therapeutic decisions for microcirculatory abnormalities and in studying of the human microcirculation

The Impact of Different Image Thresholding based Mammogram Image Segmentation- A Review

Images are examined and discretized numerical capacities. The goal of computerized image processing is to enhance the nature of pictorial data and to encourage programmed machine elucidation. A computerized imaging framework ought to have fundamental segments for picture procurement, exceptional equipment for encouraging picture applications, and a tremendous measure of memory for capacity and info/yield gadgets. Picture segmentation is the field broadly scrutinized particularly in numerous restorative applications and still offers different difficulties for the specialists. Segmentation is a critical errand to recognize districts suspicious of tumor in computerized mammograms. Every last picture have distinctive sorts of edges and diverse levels of limits. In picture transforming, the most regularly utilized strategy as a part of extricating articles from a picture is "thresholding". Thresholding is a prevalent device for picture segmentation for its straightforwardness, particularly in the fields where ongoing handling is required

Retinal vessel segmentation using textons

Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods

Detection and Classification of Diabetic Retinopathy Pathologies in Fundus Images

Diabetic Retinopathy (DR) is a disease that affects up to 80% of diabetics around the world. It is the second greatest cause of blindness in the Western world, and one of the leading causes of blindness in the U.S. Many studies have demonstrated that early treatment can reduce the number of sight-threatening DR cases, mitigating the medical and economic impact of the disease. Accurate, early detection of eye disease is important because of its potential to reduce rates of blindness worldwide. Retinal photography for DR has been promoted for decades for its utility in both disease screening and clinical research studies. In recent years, several research centers have presented systems to detect pathology in retinal images. However, these approaches apply specialized algorithms to detect specific types of lesion in the retina. In order to detect multiple lesions, these systems generally implement multiple algorithms. Furthermore, some of these studies evaluate their algorithms on a single dataset, thus avoiding potential problems associated with the differences in fundus imaging devices, such as camera resolution. These methodologies primarily employ bottom-up approaches, in which the accurate segmentation of all the lesions in the retina is the basis for correct determination. A disadvantage of bottom-up approaches is that they rely on the accurate segmentation of all lesions in order to measure performance. On the other hand, top-down approaches do not depend on the segmentation of specific lesions. Thus, top-down methods can potentially detect abnormalities not explicitly used in their training phase. A disadvantage of these methods is that they cannot identify specific pathologies and require large datasets to build their training models. In this dissertation, I merged the advantages of the top-down and bottom-up approaches to detect DR with high accuracy. First, I developed an algorithm based on a top-down approach to detect abnormalities in the retina due to DR. By doing so, I was able to evaluate DR pathologies other than microaneurysms and exudates, which are the main focus of most current approaches. In addition, I demonstrated good generalization capacity of this algorithm by applying it to other eye diseases, such as age-related macular degeneration. Due to the fact that high accuracy is required for sight-threatening conditions, I developed two bottom-up approaches, since it has been proven that bottom-up approaches produce more accurate results than top-down approaches for particular structures. Consequently, I developed an algorithm to detect exudates in the macula. The presence of this pathology is considered to be a surrogate for clinical significant macular edema (CSME), a sight-threatening condition of DR. The analysis of the optic disc is usually not taken into account in DR screening systems. However, there is a pathology called neovascularization that is present in advanced stages of DR, making its detection of crucial clinical importance. In order to address this problem, I developed an algorithm to detect neovascularization in the optic disc. These algorithms are based on amplitude-modulation and frequency-modulation (AM-FM) representations, morphological image processing methods, and classification algorithms. The methods were tested on a diverse set of large databases and are considered to be the state-of the art in this field

Advanced retinal imaging: Feature extraction, 2-D registration, and 3-D reconstruction

In this dissertation, we have studied feature extraction and multiple view geometry in the context of retinal imaging. Specifically, this research involves three components, i.e., feature extraction, 2-D registration, and 3-D reconstruction. First, the problem of feature extraction is investigated. Features are significantly important in motion estimation techniques because they are the input to the algorithms. We have proposed a feature extraction algorithm for retinal images. Bifurcations/crossovers are used as features. A modified local entropy thresholding algorithm based on a new definition of co-occurrence matrix is proposed. Then, we consider 2-D retinal image registration which is the problem of the transformation of 2-D/2-D. Both linear and nonlinear models are incorporated to account for motions and distortions. A hybrid registration method has been introduced in order to take advantages of both feature-based and area-based methods have offered along with relevant decision-making criteria. Area-based binary mutual information is proposed or translation estimation. A feature-based hierarchical registration technique, which involves the affine and quadratic transformations, is developed. After that, a 3-D retinal surface reconstruction issue has been addressed. To generate a 3-D scene from 2-D images, a camera projection or transformations of 3-D/2-D techniques have been investigated. We choose an affine camera to characterize for 3-D retinal reconstruction. We introduce a constrained optimization procedure which incorporates a geometrically penalty function and lens distortion into the cost function. The procedure optimizes all of the parameters, camera's parameters, 3-D points, the physical shape of human retina, and lens distortion, simultaneously. Then, a point-based spherical fitting method is introduced. The proposed retinal imaging techniques will pave the path to a comprehensive visual 3-D retinal model for many medical applications

Retinal Image Quality Assessment Using Supervised Classification

RÉSUMÉ L’évaluation de la qualité d’images de la rétine est une étape importante dans le diagnostic automatisé des maladies de l'œil. La précision du diagnostic est dépendante de ce critère, car une mauvaise qualité de l'image peut empêcher l'observation de caractéristiques importantes de l'oeil et des manifestations pathologiques. Un algorithme robuste est donc nécessaire afin d'évaluer la qualité des images dans une base de données. Les méthodes existantes sont généralement basées sur des paramètres de qualité d'image génériques ou des critères structurels. L’évaluation de la qualité de l'image en fonction de paramètres génériques exploite la plupart des paramètres tels que les caractéristiques de contraste, netteté, éclairage, et la texture. Ces méthodes évitent généralement les procédures de segmentation des structures de l'oeil. Le grand avantage de ces méthodes est leur faible complexité de calcul par rapport à celles mises au point sur la base de critères structurels. En revanche, les systèmes basés sur des critères structurels exigent l'identification de structures anatomiques, y compris le disque optique et le réseau vasculaire de la macula. Ces méthodes ont tendance à manquer de robustesse lorsqu'elles traitent des images de mauvaise qualité. Bien que les algorithmes d'évaluation de la qualité de l'image précédemment développé montrent de bonnes performances, ils ont été mis en place sur différentes bases de données en s'appuyant sur des critères différents. Une nouvelle mesure est donc nécessaire. Nous avons développé un algorithme d'évaluation de la qualité de l'image rétinienne basée sur l'extraction de caractéristiques génériques sans segmentation a priori de l’image. La nouveauté de cet algorithme est sa simplicité et son faible coût de calcul, ainsi que sa capacité à s’adapter sur la région d'intérêt locale. Les caractéristiques génériques sont constituées de la netteté locale, le ratio de bordure, les caractéristiques de texture et l’histogramme de couleur. La probabilité cumulée de flou métrique de détection et l'algorithme de codage de longueur variable ont été utilisés pour mesurer la netteté et les caractéristiques texturales, respectivement. Les caractéristiques extraites sont combinés pour évaluer la convenance de l'image à des fins de diagnostic. Basé sur le fait que les experts médicaux se concentrent davantage sur certaines structures anatomiques telles que la macula et le disque optique, nous avons décidé de comparer les approches globales et locales. Un « support vector machine » avec un « kernel » de fonctionsde base radiales a été utilisé comme un classificateur non linéaire afin de classer les images pour les groupes utilisables et non-utilisables. La méthodologie développée a été appliquée sur 100 images avec des tailles différentes classées par un expert médical. L'expert a évalué 50 images comme mesurable et 50 comme non-mesurable. Les résultats indiquent un très bon accord entre les prédictions de l'algorithme proposé et le jugement de l'expert médical: la sensibilité et la spécificité de l'approche locale sont respectivement 94% et 92% alors qu'il était de 87% et 89% pour l'approche globale.----------ABSTRACT Retinal image quality assessment is an important step in automated eye disease diagnosis. Diagnosis accuracy is highly dependent on the quality of retinal images, because poor image quality might prevent the observation of significant eye features and disease manifestations. A robust algorithm is therefore required in order to evaluate the quality of images in a large database. Existing algorithms for estimating the retinal image quality are generally based on generic image quality parameters or structural criteria. Image quality assessment based on generic parameters mostly exploits parameters such as contrast, sharpness, illumination, and texture features. These methods generally avoid eye structure segmentation procedures. The great advantage of these methods is their low computational complexity compared to those developed based on structural criteria. In contrast, systems based on structural criteria require identification of anatomical structures including the optic disc, macula and vascular network. These methods tend to lack in robustness when dealing with poor quality images. Although previously developed image quality assessment algorithms shows good performances, they have been implemented on different databases and relying on different criteria and a new metric is required to be developed. We developed an algorithm for retinal image quality assessment based on the extraction of generic features without images segmentation. The novelty of this algorithm is its simplicity and low computational cost and also its capability to apply on local region of interest associated with retinal images. The generic features consisting of local sharpness, edge ratio, textural features and color histogram were measured. The cumulative probability of blur detection metric and the run-length encoding algorithm were used to measure the sharpness and textural features respectively. The extracted features are combined to evaluate the image suitability for diagnosis purposes. Based on the fact that medical experts focus more on some anatomical structures such as macula and optic disc, we decided to compare global and local approaches. A support vector machine with radial basis functions was used as a nonlinear classifier in order to classify images to gradable and ungradable groups. The developed methodology was applied on 100 images with various sizes graded by a medical expert. The expert evaluated 50 images as gradable and 50 as ungradable. The results indicate a very good agreement between the proposed algorithm’s predictions and themedical expert’s judgment: the sensitivity and specificity for the local approach are respectively 94% and 92% while it was 87% and 89% for the global approach

Automatic Screening and Classification of Diabetic Retinopathy Eye Fundus Image

Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It develops to some degree in nearly all patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is essential for both early detection and early treatment. This thesis aims to investigate automatic methods for diabetic retinopathy detection and subsequently develop an effective system for the detection and screening of diabetic retinopathy. The presented diabetic retinopathy research involves three development stages. Firstly, the thesis presents the development of a preliminary classification and screening system for diabetic retinopathy using eye fundus images. The research will then focus on the detection of the earliest signs of diabetic retinopathy, which are the microaneurysms. The detection of microaneurysms at an early stage is vital and is the first step in preventing diabetic retinopathy. Finally, the thesis will present decision support systems for the detection of diabetic retinopathy and maculopathy in eye fundus images. The detection of maculopathy, which are yellow lesions near the macula, is essential as it will eventually cause the loss of vision if the affected macula is not treated in time. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. In addition to the proposed diabetic retinopathy detection systems, this thesis will present a new dataset, and will highlight the dataset collection, the expert diagnosis process and the advantages of the new dataset, compared to other public eye fundus images datasets available. The new dataset will be useful to researchers and practitioners working in the retinal imaging area and would widely encourage comparative studies in the field of diabetic retinopathy research. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy. It is also hoped that the developed automatic detection techniques will assist clinicians to diagnose diabetic retinopathy at an early stage

Detection and characterisation of vessels in retinal images.

Doctor of Philosophy in Mathematics, Statistics & Computer Science. University of KwaZulu-Natal, Durban 2015.As retinopathies such as diabetic retinopathy (DR) and retinopathy of prematurity (ROP) continue to be the major causes of blindness globally, regular retinal examinations of patients can assist in the early detection of the retinopathies. The manual detection of retinal vessels is a very tedious and time consuming task as it requires about two hours to manually detect vessels in each retinal image. Automatic vessel segmentation has been helpful in achieving speed, improved diagnosis and progress monitoring of these diseases but has been challenging due to complexities such as the varying width of the retinal vessels from very large to very small, low contrast of thin vessels with respect to background and noise due to nonhomogeneous illumination in the retinal images. Although several supervised and unsupervised segmentation methods have been proposed in the literature, the segmentation of thinner vessels, connectivity loss of the vessels and time complexity remain the major challenges. In order to address these problems, this research work investigated di erent unsupervised segmentation approaches to be used in the robust detection of large and thin retinal vessels in a timely e cient manner. Firstly, this thesis conducted a study on the use of di erent global thresholding techniques combined with di erent pre-processing and post-processing techniques. Two histogram-based global thresholding techniques namely, Otsu and Isodata were able to detect large retinal vessels but fail to segment the thin vessels because these thin vessels have very low contrast and are di cult to distinguish from the background tissues using the histogram of the retinal images. Two new multi-scale approaches of computing global threshold based on inverse di erence moment and sum-entropy combined with phase congruence are investigated to improve the detection of vessels. One of the findings of this study is that the multi-scale approaches of computing global threshold combined with phase congruence based techniques improved on the detection of large vessels and some of the thin vessels. They, however, failed to maintain the width of the detected vessels. The reduction in the width of the detected large and thin vessels results in low sensitivity rates while relatively good accuracy rates were maintained. Another study on the use of fuzzy c-means and GLCM sum entropy combined on phase congruence for vessel segmentation showed that fuzzy c-means combined with phase congruence achieved a higher average accuracy rates of 0.9431 and 0.9346 but a longer running time of 27.1 seconds when compared with the multi-scale based sum entropy thresholding combined with phase congruence with the average accuracy rates of 0.9416 and 0.9318 with a running time of 10.3 seconds. The longer running time of the fuzzy c-means over the sum entropy thresholding is, however, attributed to the iterative nature of fuzzy c-means. When compared with the literature, both methods achieved considerable faster running time. This thesis investigated two novel local adaptive thresholding techniques for the segmentation of large and thin retinal vessels. The two novel local adaptive thresholding techniques applied two di erent Haralick texture features namely, local homogeneity and energy. Although these two texture features have been applied for supervised image segmentation in the literature, their novelty in this thesis lies in that they are applied using an unsupervised image segmentation approach. Each of these local adaptive thresholding techniques locally applies a multi-scale approach on each of the texture information considering the pixel of interest in relationship with its spacial neighbourhood to compute the local adaptive threshold. The localised multi-scale approach of computing the thresholds handled the challenge of the vessels' width variation. Experiments showed significant improvements in the average accuracy and average sensitivity rates of these techniques when compared with the previously discussed global thresholding methods and state of the art. The two novel local adaptive thresholding techniques achieved a higher reduction of false vessels around the border of the optic disc when compared with some of the previous techniques in the literature. These techniques also achieved a highly improved computational time of 1.9 to 3.9 seconds to segment the vessels in each retinal image when compared with the state of the art. Hence, these two novel local adaptive thresholding techniques are proposed for the segmentation of the vessels in the retinal images. This thesis further investigated the combination of di erence image and kmeans clustering technique for the segmentation of large and thin vessels in retinal images. The pre-processing phase computed a di erence image and k-means clustering technique was used for the vessel detection. While investigating this vessel segmentation method, this thesis established the need for a difference image that preserves the vessel details of the retinal image. Investigating the di erent low pass filters, median filter yielded the best di erence image required by k-means clustering for the segmentation of the retinal vessels. Experiments showed that the median filter based di erence images combined with k-means clustering technique achieved higher average accuracy and average sensitivity rates when compared with the previously discussed global thresholding methods and the state of the art. The median filter based di erence images combined with k-means clustering technique (that is, DIMDF) also achieved a higher reduction of false vessels around the border of the optic disc when compared with some previous techniques in the literature. These methods also achieved a highly improved computational time of 3.4 to 4 seconds when compared with the literature. Hence, the median filter based di erence images combined with k-means clustering technique are proposed for the segmentation of the vessels in retinal images. The characterisation of the detected vessels using tortuosity measure was also investigated in this research. Although several vessel tortuosity methods have been discussed in the literature, there is still need for an improved method that e ciently detects vessel tortuosity. The experimental study conducted in this research showed that the detection of the stationary points helps in detecting the change of direction and twists in the vessels. The combination of the vessel twist frequency obtained using the stationary points and distance metric for the computation of normalised and nonnormalised tortuosity index (TI) measure was investigated. Experimental results showed that the non-normalised TI measure had a stronger correlation with the expert's ground truth when compared with the distance metric and normalised TI measures. Hence, a non-normalised TI measure that combines the vessel twist frequency based on the stationary points and distance metric is proposed for the measurement of vessel tortuosity

A novel automated approach of multi-modality retinal image registration and fusion

Biomedical image registration and fusion are usually scene dependent, and require intensive computational effort. A novel automated approach of feature-based control point detection and area-based registration and fusion of retinal images has been successfully designed and developed. The new algorithm, which is reliable and time-efficient, has an automatic adaptation from frame to frame with few tunable threshold parameters. The reference and the to-be-registered images are from two different modalities, i.e. angiogram grayscale images and fundus color images. The relative study of retinal images enhances the information on the fundus image by superimposing information contained in the angiogram image. Through the thesis research, two new contributions have been made to the biomedical image registration and fusion area. The first contribution is the automatic control point detection at the global direction change pixels using adaptive exploratory algorithm. Shape similarity criteria are employed to match the control points. The second contribution is the heuristic optimization algorithm that maximizes Mutual-Pixel-Count (MPC) objective function. The initially selected control points are adjusted during the optimization at the sub-pixel level. A global maxima equivalent result is achieved by calculating MPC local maxima with an efficient computation cost. The iteration stops either when MPC reaches the maximum value, or when the maximum allowable loop count is reached. To our knowledge, it is the first time that the MPC concept has been introduced into biomedical image fusion area as the measurement criteria for fusion accuracy. The fusion image is generated based on the current control point coordinates when the iteration stops. The comparative study of the presented automatic registration and fusion scheme against Centerline Control Point Detection Algorithm, Genetic Algorithm, RMSE objective function, and other existing data fusion approaches has shown the advantage of the new approach in terms of accuracy, efficiency, and novelty