Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation

In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations

Application of active contours with expert knowledge to heart ventricle segmentation

Automatic heart ventricle segmentation in CT heart images can be an element of system supporting pulmonary embolism diagnosis. To solve that problem in this paper an application of two classical active contour models, snakes and geometric active contours, is proposed. The prepared implementation uses the unified model of those techniques which allows to define forces acting upon a contour only once. The nature of the images causes that the process of force construction requires additional expert knowledge since using only the information visible in the image satisfactory results cannot be obtained

Spatch based active partitions with linguistically formulated energy

The present paper shows the method of cognitive hierarchical active partitions that can be applied to creation of automatic image understanding systems. The approach, which stems from active contours techniques, allows one to use not only the knowledge contained in an image, but also any additional expert knowledge. Special emphasis is put on the effcient way of knowledge retrieval, which could minimise the necessity to render information expressed in a natural language into a description convenient for recognition algorithms and machine learning

Robust Real-Time Segmentation of Images and Videos Using a Smooth-Spline Snake-Based Algorithm

International audienc

Multiresolution Subdivision Snakes

We present a new family of snakes that satisfy the property of multiresolution by exploiting subdivision schemes. We show in a generic way how to construct such snakes based on an admissible subdivision mask. We derive the necessary energy formulations and provide the formulas for their efficient computation. Depending on the choice of the mask, such models have the ability to reproduce trigonometric or polynomial curves. They can also be designed to be interpolating, a property that is useful in user-interactive applications. We provide explicit examples of subdivision snakes and illustrate their use for the segmentation of bioimages. We show that they are robust in the presence of noise and provide a multiresolution algorithm to enlarge their basin of attraction, which decreases their dependence on initialization compared to singleresolution snakes. We show the advantages of the proposed model in terms of computation and segmentation of structures with different sizes

Design and implementation of a program for segmenting and automated analysis of macular lesions

Mezi nejzávažnější oční onemocnění bezesporu patří věkem podmíněná makulární degenerace, která je onemocněním, které může u lidí nad 60 let způsobovat slepotu. Vlhká forma tohoto onemocnění je závažnější, ale existují způsoby terapie, které dokáží zastavit její progresi. U suché formy věkem podmíněné makulární degenerace lze jen doporučit příjem vitamínů pro správnou funkci retinálních buněk. Z tohoto důvodu je velmi důležité tyto pacienty sledovat, vyvíjet nástroje a hledat způsoby, jak pozastavit progresi vlhké i suché formy makulární degenerace. Praktická část této práce se zaměřuje na vývoj algoritmu, který zvládne automatizovaně ohraničit makulární lézi pomocí parametrické aktivní kontury a vyhodnotit velikost plochy očního pozadí, kterou zasahuje a tím podává informaci o stavu makulárního onemocnění.Age related macular degeneration is one of the most challenging eye dissease which is cause of blindness of people older than 60 years. Exsudative form of AMD is more difficult form nevertheless there are therapeutic methods for stopping its progression, Different form of the disease is dry form which is not possible to treat – the only way to influence its progression is using vitamins for proper cell metabolism. Special focus is now on new segmentation methods which can help physicians to evaluate stage of the disease and how to treat it. This thesis is focusing on development of segmentation of AMD in clinical pictures using parametric active contours. Segmented picture is evaluated to get the information about lesion.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř

Parametric shape processing in biomedical imaging

In this thesis, we present a coherent and consistent approach for the estimation of shape and shape attributes from noisy images. As compared to the traditional sequential approach, our scheme is centered on a shape model which drives the feature extraction, shape optimization, and the attribute evaluation modules. In the first section, we deal with the detection of image features that guide the shape-extraction process. We propose a general approach for the design of 2-D feature detectors from a class of steerable functions, based on the optimization of a Canny-like criterion. As compared to previous computational designs, our approach is truly 2-D and yields more orientation selective detectors. We then address the estimation of the global shape from an image. Specifically, we propose to use cubic-spline-based parametric active contour models to solve two shape-extraction problems: (i) the segmentation of closed objects and (ii) the 3-D reconstruction of DNA filaments from their stereo cryo-electron micrographs. We present several enhancements of existing snake algorithms for segmentation. For the detection of 3-D DNA filaments from their orthogonal projections, we introduce the concept of projection-steerable matched filtering. We then use a 3-D snake algorithm to reconstruct the shape. Next, we analyze the efficiency of curve representations using refinable basis functions for the description of shape boundaries. We derive an exact expression for the error when we approximate a periodic signal in a scaling-function basis. Finally, we present a method for the exact computation of the area moments of such shapes