Stream implementation of serial morphological filters with approximated polygons

ISBN : 978-142448157-6International audienceThis paper describes an original stream implementation of serially composed morphological filters using approximated flat polygons. It strictly respects a sequential data access. Results are obtained with minimal latency while operating within minimal memory space; even for very large neighborhoods. This is interesting for serially composed advanced filters, such as Alternating Sequential Filters or granulometries. We show how the dedicated implementation on an FPGA allows obtaining a previously unequaled performance, opening an opportunity to use these operators in time-critical, high-end applications

Hierarchical stack filtering : a bitplane-based algorithm for massively parallel processors

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition, and then process every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement

Resource Efficient Hardware Architecture for Fast Computation of Running Max/Min Filters

Running max/min filters on rectangular kernels are widely used in many digital signal and image processing applications. Filtering with a k×k kernel requires of k2−1 comparisons per sample for a direct implementation; thus, performance scales expensively with the kernel size k. Faster computations can be achieved by kernel decomposition and using constant time one-dimensional algorithms on custom hardware. This paper presents a hardware architecture for real-time computation of running max/min filters based on the van Herk/Gil-Werman (HGW) algorithm. The proposed architecture design uses less computation and memory resources than previously reported architectures when targeted to Field Programmable Gate Array (FPGA) devices. Implementation results show that the architecture is able to compute max/min filters, on 1024×1024 images with up to 255×255 kernels, in around 8.4 milliseconds, 120 frames per second, at a clock frequency of 250 MHz. The implementation is highly scalable for the kernel size with good performance/area tradeoff suitable for embedded applications. The applicability of the architecture is shown for local adaptive image thresholding

Efficient 2-D Grayscale Morphological Transformations With Arbitrary Flat Structuring Elements

An efficient algorithm is presented for the computation of grayscale morphological operations with arbitrary 2-D flat structuring elements (S.E.). The required computing time is independent of the image content and of the number of gray levels used. It always outperforms the only existing comparable method, which was proposed in earlier work, by a factor between 3.5 and 35.1, depending on the image type and shape of S.E. So far, filtering using multiple S.E.s is always done by performing the operator for each size and shape of the S.E. separately. With our method, filtering with multiple S.E.s can be performed by a single operator for a slightly reduced computational cost per size or shape, which makes this method more suitable for use in granulometries, dilation-erosion scale spaces, and template matching using the hit-or-miss transform. The discussion focuses on erosions and dilations, from which other transformations can be derived.

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