Fast interactive 2D and 3D segmentation tools.

by Kevin Chun-Ho Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 74-79).Abstract also in Chinese.Chinese Abstract --- p.vAbstract --- p.viAcknowledgements --- p.viiChapter 1 --- Introduction --- p.1Chapter 2 --- Prior Work : Image Segmentation Techniques --- p.3Chapter 2.1 --- Introduction to Image Segmentation --- p.4Chapter 2.2 --- Region Based Segmentation --- p.5Chapter 2.2.1 --- Boundary Based vs Region Based --- p.5Chapter 2.2.2 --- Region growing --- p.5Chapter 2.2.3 --- Integrating Region Based and Edge Detection --- p.6Chapter 2.2.4 --- Watershed Based Methods --- p.8Chapter 2.3 --- Fuzzy Set Theory in Segmentation --- p.8Chapter 2.3.1 --- Fuzzy Geometry Concept --- p.8Chapter 2.3.2 --- Fuzzy C-Means (FCM) Clustering --- p.9Chapter 2.4 --- Canny edge filter with contour following --- p.11Chapter 2.5 --- Pyramid based Fast Curve Extraction --- p.12Chapter 2.6 --- Curve Extraction with Multi-Resolution Fourier transformation --- p.13Chapter 2.7 --- User interfaces for Image Segmentation --- p.13Chapter 2.7.1 --- Intelligent Scissors --- p.14Chapter 2.7.2 --- Magic Wands --- p.16Chapter 3 --- Prior Work : Active Contours Model (Snakes) --- p.17Chapter 3.1 --- Introduction to Active Contour Model --- p.18Chapter 3.2 --- Variants and Extensions of Snakes --- p.19Chapter 3.2.1 --- Balloons --- p.20Chapter 3.2.2 --- Robust Dual Active Contour --- p.21Chapter 3.2.3 --- Gradient Vector Flow Snakes --- p.22Chapter 3.2.4 --- Energy Minimization using Dynamic Programming with pres- ence of hard constraints --- p.23Chapter 3.3 --- Conclusions --- p.25Chapter 4 --- Slimmed Graph --- p.26Chapter 4.1 --- BSP-based image analysis --- p.27Chapter 4.2 --- Split Line Selection --- p.29Chapter 4.3 --- Split Line Selection with Summed Area Table --- p.29Chapter 4.4 --- Neighbor blocks --- p.31Chapter 4.5 --- Slimmed Graph Generation --- p.32Chapter 4.6 --- Time Complexity --- p.35Chapter 4.7 --- Results and Conclusions --- p.36Chapter 5 --- Fast Intelligent Scissor --- p.38Chapter 5.1 --- Background --- p.39Chapter 5.2 --- Motivation of Fast Intelligent Scissors --- p.39Chapter 5.3 --- Main idea of Fast Intelligent Scissors --- p.40Chapter 5.3.1 --- Node position and Cost function --- p.41Chapter 5.4 --- Implementation and Results --- p.42Chapter 5.5 --- Conclusions --- p.43Chapter 6 --- 3D Contour Detection: Volume Cutting --- p.50Chapter 6.1 --- Interactive Volume Cutting with the intelligent scissors --- p.51Chapter 6.2 --- Contour Selection --- p.52Chapter 6.2.1 --- 3D Intelligent Scissors --- p.53Chapter 6.2.2 --- Dijkstra's algorithm --- p.54Chapter 6.3 --- 3D Volume Cutting --- p.54Chapter 6.3.1 --- Cost function for the cutting surface --- p.55Chapter 6.3.2 --- "Continuity function (x,y, z) " --- p.59Chapter 6.3.3 --- Finding the cutting surface --- p.61Chapter 6.3.4 --- Topological problems for the volume cutting --- p.61Chapter 6.3.5 --- Assumptions for the well-conditional contour used in our algo- rithm --- p.62Chapter 6.4 --- Implementation and Results --- p.64Chapter 6.5 --- Conclusions --- p.64Chapter 7 --- Conclusions --- p.71Chapter 7.1 --- Contributions --- p.71Chapter 7.2 --- Future Work --- p.72Chapter 7.2.1 --- Real-time interactive tools with Slimmed Graph --- p.72Chapter 7.2.2 --- 3D slimmed graph --- p.72Chapter 7.2.3 --- Cartoon Film Generation System --- p.7

GBM Volumetry using the 3D Slicer Medical Image Computing Platform

Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer – a free platform for biomedical research – provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 ± 5.23% and a Hausdorff Distance of 2.32 ± 5.23 mm

Design Simulation and Assessment of Cellular Automata Based Improved Image Segmentation System

A variety of methods may be found in the numerous image segmentation techniques. Here a method of text retrieval conducted is typically to produce a collection of localized features. In computer science, object recognition is the problem of automatically "identifying", or classifying, an object. In certain instances, the awareness of artifacts is deeper into image in image segmentation through image processing. The algorithm used for image segmentation has a direct impact on the outcome of the whole approach, therefore it is important to choose carefully. It is important to choose a segmentation method appropriate for a certain framework. There are several ready-to-use segmentation methods, so one by one evaluate the tools to see which works best. Segmentation algorithms have reached such a level of complexity that a research employing them is often considered impractical. The given research undertakes the process of improved graph cut method to select the foreground and background of image through labelling and segmentation of the image. Results have been compared on the performance parameter to analyse the effectiveness of the proposed algorithm for segmentation of the images

Hybrid Intelligent System for Diagnosing Breast Pre-Cancerous and Cancerous Conditions Based on Image Analysis

Modern diagnostic technologies are automated microscopy systems (AMSs). In this research study, the authors analyzed the modern AMS methods and algorithms. Criteria-based comparative analysis of AMS has been made, and their advantages and disadvantages have been identified at the three main levels of image processing. This allowed determining the main direction of such systems development, that is, designing the hybrid intelligent AMS. The work of an expert physician implies visual image interpretation, selection of qualitative features of micro-objects, the formation of diagnostic rules based on expert knowledge, and making diagnoses. Knowledge introduction model contains a productive model, in which knowledge is presented in the form of rules expressed in productive pseudo code if-then. Logic inference machine is a module designed to logically derive the facts and rules from the base according to the laws of formal logic. A set of modern methods and algorithms for low-, mid-, and high-level image processing have been used in the AMS structure

Automatic video segmentation employing object/camera modeling techniques

Practically established video compression and storage techniques still process video sequences as rectangular images without further semantic structure. However, humans watching a video sequence immediately recognize acting objects as semantic units. This semantic object separation is currently not reflected in the technical system, making it difficult to manipulate the video at the object level. The realization of object-based manipulation will introduce many new possibilities for working with videos like composing new scenes from pre-existing video objects or enabling user-interaction with the scene. Moreover, object-based video compression, as defined in the MPEG-4 standard, can provide high compression ratios because the foreground objects can be sent independently from the background. In the case that the scene background is static, the background views can even be combined into a large panoramic sprite image, from which the current camera view is extracted. This results in a higher compression ratio since the sprite image for each scene only has to be sent once. A prerequisite for employing object-based video processing is automatic (or at least user-assisted semi-automatic) segmentation of the input video into semantic units, the video objects. This segmentation is a difficult problem because the computer does not have the vast amount of pre-knowledge that humans subconsciously use for object detection. Thus, even the simple definition of the desired output of a segmentation system is difficult. The subject of this thesis is to provide algorithms for segmentation that are applicable to common video material and that are computationally efficient. The thesis is conceptually separated into three parts. In Part I, an automatic segmentation system for general video content is described in detail. Part II introduces object models as a tool to incorporate userdefined knowledge about the objects to be extracted into the segmentation process. Part III concentrates on the modeling of camera motion in order to relate the observed camera motion to real-world camera parameters. The segmentation system that is described in Part I is based on a background-subtraction technique. The pure background image that is required for this technique is synthesized from the input video itself. Sequences that contain rotational camera motion can also be processed since the camera motion is estimated and the input images are aligned into a panoramic scene-background. This approach is fully compatible to the MPEG-4 video-encoding framework, such that the segmentation system can be easily combined with an object-based MPEG-4 video codec. After an introduction to the theory of projective geometry in Chapter 2, which is required for the derivation of camera-motion models, the estimation of camera motion is discussed in Chapters 3 and 4. It is important that the camera-motion estimation is not influenced by foreground object motion. At the same time, the estimation should provide accurate motion parameters such that all input frames can be combined seamlessly into a background image. The core motion estimation is based on a feature-based approach where the motion parameters are determined with a robust-estimation algorithm (RANSAC) in order to distinguish the camera motion from simultaneously visible object motion. Our experiments showed that the robustness of the original RANSAC algorithm in practice does not reach the theoretically predicted performance. An analysis of the problem has revealed that this is caused by numerical instabilities that can be significantly reduced by a modification that we describe in Chapter 4. The synthetization of static-background images is discussed in Chapter 5. In particular, we present a new algorithm for the removal of the foreground objects from the background image such that a pure scene background remains. The proposed algorithm is optimized to synthesize the background even for difficult scenes in which the background is only visible for short periods of time. The problem is solved by clustering the image content for each region over time, such that each cluster comprises static content. Furthermore, it is exploited that the times, in which foreground objects appear in an image region, are similar to the corresponding times of neighboring image areas. The reconstructed background could be used directly as the sprite image in an MPEG-4 video coder. However, we have discovered that the counterintuitive approach of splitting the background into several independent parts can reduce the overall amount of data. In the case of general camera motion, the construction of a single sprite image is even impossible. In Chapter 6, a multi-sprite partitioning algorithm is presented, which separates the video sequence into a number of segments, for which independent sprites are synthesized. The partitioning is computed in such a way that the total area of the resulting sprites is minimized, while simultaneously satisfying additional constraints. These include a limited sprite-buffer size at the decoder, and the restriction that the image resolution in the sprite should never fall below the input-image resolution. The described multisprite approach is fully compatible to the MPEG-4 standard, but provides three advantages. First, any arbitrary rotational camera motion can be processed. Second, the coding-cost for transmitting the sprite images is lower, and finally, the quality of the decoded sprite images is better than in previously proposed sprite-generation algorithms. Segmentation masks for the foreground objects are computed with a change-detection algorithm that compares the pure background image with the input images. A special effect that occurs in the change detection is the problem of image misregistration. Since the change detection compares co-located image pixels in the camera-motion compensated images, a small error in the motion estimation can introduce segmentation errors because non-corresponding pixels are compared. We approach this problem in Chapter 7 by integrating risk-maps into the segmentation algorithm that identify pixels for which misregistration would probably result in errors. For these image areas, the change-detection algorithm is modified to disregard the difference values for the pixels marked in the risk-map. This modification significantly reduces the number of false object detections in fine-textured image areas. The algorithmic building-blocks described above can be combined into a segmentation system in various ways, depending on whether camera motion has to be considered or whether real-time execution is required. These different systems and example applications are discussed in Chapter 8. Part II of the thesis extends the described segmentation system to consider object models in the analysis. Object models allow the user to specify which objects should be extracted from the video. In Chapters 9 and 10, a graph-based object model is presented in which the features of the main object regions are summarized in the graph nodes, and the spatial relations between these regions are expressed with the graph edges. The segmentation algorithm is extended by an object-detection algorithm that searches the input image for the user-defined object model. We provide two objectdetection algorithms. The first one is specific for cartoon sequences and uses an efficient sub-graph matching algorithm, whereas the second processes natural video sequences. With the object-model extension, the segmentation system can be controlled to extract individual objects, even if the input sequence comprises many objects. Chapter 11 proposes an alternative approach to incorporate object models into a segmentation algorithm. The chapter describes a semi-automatic segmentation algorithm, in which the user coarsely marks the object and the computer refines this to the exact object boundary. Afterwards, the object is tracked automatically through the sequence. In this algorithm, the object model is defined as the texture along the object contour. This texture is extracted in the first frame and then used during the object tracking to localize the original object. The core of the algorithm uses a graph representation of the image and a newly developed algorithm for computing shortest circular-paths in planar graphs. The proposed algorithm is faster than the currently known algorithms for this problem, and it can also be applied to many alternative problems like shape matching. Part III of the thesis elaborates on different techniques to derive information about the physical 3-D world from the camera motion. In the segmentation system, we employ camera-motion estimation, but the obtained parameters have no direct physical meaning. Chapter 12 discusses an extension to the camera-motion estimation to factorize the motion parameters into physically meaningful parameters (rotation angles, focal-length) using camera autocalibration techniques. The speciality of the algorithm is that it can process camera motion that spans several sprites by employing the above multi-sprite technique. Consequently, the algorithm can be applied to arbitrary rotational camera motion. For the analysis of video sequences, it is often required to determine and follow the position of the objects. Clearly, the object position in image coordinates provides little information if the viewing direction of the camera is not known. Chapter 13 provides a new algorithm to deduce the transformation between the image coordinates and the real-world coordinates for the special application of sport-video analysis. In sport videos, the camera view can be derived from markings on the playing field. For this reason, we employ a model of the playing field that describes the arrangement of lines. After detecting significant lines in the input image, a combinatorial search is carried out to establish correspondences between lines in the input image and lines in the model. The algorithm requires no information about the specific color of the playing field and it is very robust to occlusions or poor lighting conditions. Moreover, the algorithm is generic in the sense that it can be applied to any type of sport by simply exchanging the model of the playing field. In Chapter 14, we again consider panoramic background images and particularly focus ib their visualization. Apart from the planar backgroundsprites discussed previously, a frequently-used visualization technique for panoramic images are projections onto a cylinder surface which is unwrapped into a rectangular image. However, the disadvantage of this approach is that the viewer has no good orientation in the panoramic image because he looks into all directions at the same time. In order to provide a more intuitive presentation of wide-angle views, we have developed a visualization technique specialized for the case of indoor environments. We present an algorithm to determine the 3-D shape of the room in which the image was captured, or, more generally, to compute a complete floor plan if several panoramic images captured in each of the rooms are provided. Based on the obtained 3-D geometry, a graphical model of the rooms is constructed, where the walls are displayed with textures that are extracted from the panoramic images. This representation enables to conduct virtual walk-throughs in the reconstructed room and therefore, provides a better orientation for the user. Summarizing, we can conclude that all segmentation techniques employ some definition of foreground objects. These definitions are either explicit, using object models like in Part II of this thesis, or they are implicitly defined like in the background synthetization in Part I. The results of this thesis show that implicit descriptions, which extract their definition from video content, work well when the sequence is long enough to extract this information reliably. However, high-level semantics are difficult to integrate into the segmentation approaches that are based on implicit models. Intead, those semantics should be added as postprocessing steps. On the other hand, explicit object models apply semantic pre-knowledge at early stages of the segmentation. Moreover, they can be applied to short video sequences or even still pictures since no background model has to be extracted from the video. The definition of a general object-modeling technique that is widely applicable and that also enables an accurate segmentation remains an important yet challenging problem for further research

Live Wire Segmentation Tool for Osteophyte Detection in Lumbar Spine X-Ray Images

Computer-assisted vertebra segmentation in x-ray images is a challenging problem. Inter-subject variability and the generally poor contrast of digitized radiograph images contribute to the segmentation difficulty. In this paper, a semi-automated live wire approach is investigated for vertebrae segmentation. The live wire approach integrates initially selected user points with dynamic programming to generate a closed vertebra boundary. In order to assess the degree to which vertebra features are conserved using the live wire technique, convex hull-based features to characterize anterior osteophytes in lumbar vertebrae are determined for live wire and manually segmented vertebrae. Anterior osteophyte discrimination was performed over 405 lumbar vertebrae, 204 abnormal vertebrae with anterior osteophytes and 201 normal vertebrae. A leave-one-out standard back propagation neural network was used for vertebrae segmentation. Experimental results show that manual segmentation yielded slightly better discrimination results than the live wire technique