Textured Renyl Entropy for Image Thresholding

This paper introduces Textured Renyi Entropy for image thresholding based on a novel combination mechanism

Segmentasi Exudate pada Citra Fundus Menggunakan Mathematical Morphology dan Kombinasi Renyi Entropy Thresholding dengan Cuckoo Search Optimization Algorithm.

Diabetic retinopathy merupakan penyakit yang disebabkan oleh diabetes, yang menyebabkan abnormalitas dari pembuluh darah retina. Salah satunya exudates yang merupakan lapisan lemak dan protein yang pecah pada pembuluh darah yang abnormal dan bisa menyebabkan kebutaan bila berada pada area macula. Sebelum mendeteksi exudates, area optic disk pada retina perlu dideteksi dan dihilangkan, karena optic disk juga memiliki nilai intensitas yang hampir sama dengan exudates. Area seperti pembuluh darah dan haemorhage juga perlu dihilangkan karena area tersebut tidak berkaitan dengan proses segmentasi exudates. Penelitian ini mengusulkan sebuah metode untuk segmentasi exudates pada citra fundus menggunakan mathematical morphology dan kombinasi renyi entropy thresholding dengan cuckoo search optimization algorithm. Mathematical morphology untuk mendeteksi exudates, akan tetapi metode morfologi saja tidak cukup untuk mengurangi over segmentasi sehingga untuk mengurangi oversegmentasi dikembangkan metode renyi entropy thresholding yang mempertimbangkan intensitas dari gambar. Metode renyi entropi thresholding dikombinasikan dengan metode optimasi cuckoo search algorithm. Pengembangan metode renyi entropy thresholding dilakukan agar dapat menghasilkan nilai threshold lebih optimal dengan mengoptimalkan nilai parameter rho pada renyi entropy yang ditetapkan antara nilai 0-1 menjadi adaptif menggunakan pendekatan cucko search algorithm. Sehingga metode yang dihasilkan menjadi renyi entropy thresholding berdasarkan cucko search optimization algorithm. Pengujian dilakukan pada gambar diaretdb1. Dataset diolah berdasarkan metode yang diajukan dengan menghitung nilai sensitivity, specificity dan accuracy dengan nilai berturut-turut 92,26%, 99,77% dan 99,72%. ========================================================================================= Diabetic retinopathy is a disease caused by diabetes, which is caused by abnormalities of the blood vessels in the eyes. One of them are exudates which are fat that broken on the abnormal blood vessels and can lead to blindness. Before detecting exudates, optic disk area is detected and removed since it has similar intensity with exudates. Mathematical morphology is used to detect the area of exudates and remove the area of the optic disk, because the morphological process still results oversegmentation then thresholding is done to reduce over-segmentation. Area such as blood vessels and haemorhage need to be removed because the area is not related to the exudates segmentation process. This research proposes a method for segmentation of exudates on the fundus image using mathematical morphology and renyi entropy thresholding combination with cuckoo search optimization algorithm. Mathematical morphology is to detect exudates, but morphological methods are not sufficient to reduce over segmentation. The over-segmentation is reduced using renyi entropy thresholding method which counts value of the intensity of the image. Renyi entropy thresholding method combined with cuckoo search algorithm optimization method. The method of renyi entropy thresholding is employed in order to get more optimal threshold value by optimizing the value of rho parameter on renyi entropy which the value is between 0-1 to be adaptive using cuckoo search algorithm approach. So the method becomes renyi entropy thresholding based on cuckoo search optimization algorithm. The test is performed on diaretdb1 image. The dataset is processed by the proposed method by calculating the sensitivity, specificity and accuracy value with the results 92.26%, 99.77% and 99.72% respectively

Diverse developmental trajectories of perineuronal nets during vertebrate nervous system construction

2018 Spring.Includes bibliographical references.In the central nervous system, aggregated extracellular matrix compounds known as perineuronal nets (PNNs) shape patterns of neural connectivity over development. Removing PNNs restores juvenile-like states of neural circuit plasticity and subsequent behavioral plasticity. Our current understanding of the role of PNNs in plasticity has resulted in promising therapeutic applications for many neurodegenerative diseases. To ensure safety and efficacy in such applications, we require a broad understanding of PNN function in the nervous system. The current data suggest that PNNs stabilize fundamental features of neural connectivity progressively in an ascending, or "ground-up", fashion. Stabilizing lower input processing pathways establishes a solid, reliable foundation for higher cognition. However, data on PNN development exists almost exclusively for mammals. Is, then, the ground-up model of circuit stabilization a general feature of PNNs across vertebrates? I found that developmental patterns of PNNs in fish (Poecilia reticulata), amphibians (Rhinella yunga), and reptiles (Anolis sagrei) follow diverse trajectories, often emerging first in higher forebrain processing pathways. Similarly, they associate with diverse cell populations and vary widely in structural characteristics both within and across species. While my data do not invalidate a ground-up model for mammal PNNs, they do suggest that this pattern may be an evolutionary innovation in this group, and that the broad roles of PNNs in circuit stability and neuronal physiology are complex and lineage-specific

Content-based Image Retrieval by Information Theoretic Measure

Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset.Defence Science Journal, 2011, 61(5), pp.415-430, DOI:http://dx.doi.org/10.14429/dsj.61.117

Towards automated and objective assessment of fabric pilling

Pilling is a complex property of textile fabrics, representing, for the final user, a non-desired feature to be controlled and measured by companies working in the textile industry. Traditionally, pilling is assessed by visually comparing fabrics with reference to a set of standard images, thus often resulting in inconsistent quality control. A number of methods using machine vision have been proposed all over the world, with almost all sharing the idea that pilling can be assessed by determining the number of pills or the area occupied by the pills on the fabric surface. In the present work a different approach is proposed: instead of determining the number of pills, a machine vision-based procedure is devised with the aim of extracting a number of parameters characterizing the fabric. These are then used to train an artificial neural network to automatically grade the fabrics in terms of pilling. Tested against a set of differently pilled fabrics, the method shows its effectiveness

image analysis and processing with applications in proteomics and medicine

This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models and more specific region-based active contours. Two different objectives are pursued. The first is the core issue of unsupervised parameterization in image segmentation, whereas the second is the formulation of a complete model for the segmentation of proteomics images, which is the first to exploit the appealing attributes of active contours. The first major contribution of this thesis is a novel framework for the automated parameterization of region-based active contours. The presented framework aims to endow segmentation results with objectivity and robustness as well as to set domain users free from the cumbersome and time-consuming process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of the acquisition devices. The experimental results demonstrate that the presented framework maintains a segmentation quality which is comparable to the one obtained with empirical parameterization. The second major contribution of this thesis is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues in 2D-GE image analysis including streaks, artifacts, faint and overlapping spots. In addition, it provides an alternate to the laborious, error-prone process of manual editing, which is required in state-of-the-art 2D-GE image analysis software packages. The experimental results demonstrate that the presented model outperforms 2D-GE image analysis software packages in terms of detection and segmentation quantity metrics

Image Analysis and Processing With Applications in Proteomics and Medicine

Στην παρούσα διατριβή παρουσιάζονται αυτόματοι αλγόριθμοι ανάλυσης εικόνας για την κατάτμηση διαφόρων τύπων εικόνων, με έμφαση στις εικόνες πρωτεομικής και στις ιατρικές εικόνες. Οι προτεινόμενοι αλγόριθμοι βασίζονται στις αρχές των παραμορφώσιμων μοντέλων. Η διατριβή εστιάζει σε δύο κυρίως στόχους: 1) στην επίλυση του σημαντικού προβλήματος της αυτόματης παραμετροποίησης στην κατάτμηση εικόνας, 2) στην διατύπωση ενός ολοκληρωμένου μοντέλου κατάτμησης εικόνων πρωτεομικής. Η πρώτη συνεισφορά είναι ένα πρωτότυπο πλαίσιο αυτόματης παραμετροποίησης των ενεργών περιγραμμάτων περιοχής. Το πλαίσιο εμπλουτίζει τα αποτελέσματα με αντικειμενικότητα και απελευθερώνει τους τελικούς χρήστες από την επίπονη διαδικασία της εμπειρικής ρύθμισης. Εφαρμόζεται σε διάφορους τύπους ιατρικών εικόνων και παραμένει ανεπηρέαστο στις τροποποιήσεις των ρυθμίσεων των συσκευών λήψης των εικόνων αυτών. Τα πειραματικά αποτελέσματα καταδεικνύουν ότι το προτεινόμενο πλαίσιο διατηρεί υψηλή την ποιότητα κατάτμησης, συγκρίσιμη με εκείνη που επιτυγχάνεται με εμπειρική παραμετροποίηση. Η δεύτερη συνεισφορά είναι ένα αυτόματο μοντέλο βασιζόμενο στα ενεργά περιγράμματα για την κατάτμηση εικόνων πρωτεομικής. Το μοντέλο αντιμετωπίζει σημαντικά προβλήματα συμπεριλαμβανομένων των γραμμών, τεχνουργημάτων, αχνών και επικαλυπτομένων κηλίδων. Ακόμη, παρέχει εναλλακτική λύση στην επιρρεπή σε σφάλματα διαδικασία της χειρωνακτικής επεξεργασίας που απαιτείται στα υπάρχοντα πακέτα λογισμικού. Τα πειραματικά αποτελέσματα καταδεικνύουν ότι το προτεινόμενο μοντέλο υπερτερεί των υπαρχόντων πακέτων λογισμικού σε ποσοτικές μετρικές εντοπισμού και κατάτμησης.This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models. Two objectives are pursued: 1) the core issue of unsupervised parameterization in image segmentation, 2) the formulation of a complete model for the segmentation of proteomics images. The first contribution is a novel framework for automated parameterization of region-based active contours. The presented framework endows segmentation results with objectivity and sets domain users free from the cumbersome process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of acquisition devices. The experimental results demonstrate that the presented framework maintains a high segmentation quality, comparable to the one obtained with empirical parameterization. The second contribution is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues including streaks, artifacts, faint and overlapping spots. Moreover, it provides an alternate to the error-prone process of manual editing, required in state-of-the-art software packages. The experimental results demonstrate that the proposed model outperforms software packages in terms of detection and segmentation quantity metrics

An Information Theoretic Framework for Camera and Lidar Sensor Data Fusion and its Applications in Autonomous Navigation of Vehicles.

This thesis develops an information theoretic framework for multi-modal sensor data fusion for robust autonomous navigation of vehicles. In particular we focus on the registration of 3D lidar and camera data, which are commonly used perception sensors in mobile robotics. This thesis presents a framework that allows the fusion of the two modalities, and uses this fused information to enhance state-of-the-art registration algorithms used in robotics applications. It is important to note that the time-aligned discrete signals (3D points and their reflectivity from lidar, and pixel location and color from camera) are generated by sampling the same physical scene, but in a different manner. Thus, although these signals look quite different at a high level (2D image from a camera looks entirely different than a 3D point cloud of the same scene from a lidar), since they are generated from the same physical scene, they are statistically dependent upon each other at the signal level. This thesis exploits this statistical dependence in an information theoretic framework to solve some of the common problems encountered in autonomous navigation tasks such as sensor calibration, scan registration and place recognition. In a general sense we consider these perception sensors as a source of information (i.e., sensor data), and the statistical dependence of this information (obtained from different modalities) is used to solve problems related to multi-modal sensor data registration.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107286/1/pgaurav_1.pd