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

Machine learned boundary definitions for an expert's tracing assistant in image processing

Department Head: Anton Willem Bohm.Includes bibliographical references (pages 178-184).Most image processing work addressing boundary definition tasks embeds the assumption that an edge in an image corresponds to the boundary of interest in the world. In straightforward imagery this is true, however it is not always the case. There are images in which edges are indistinct or obscure, and these images can only be segmented by a human expert. The work in this dissertation addresses the range of imagery between the two extremes of those straightforward images and those requiring human guidance to appropriately segment. By freeing systems of a priori edge definitions and building in a mechanism to learn the boundary definitions needed, systems can do better and be more broadly applicable. This dissertation presents the construction of such a boundary-learning system and demonstrates the validity of this premise on real data. A framework was created for the task in which expert-provided boundary exemplars are used to create training data, which in turn are used by a neural network to learn the task and replicate the expert's boundary tracing behavior. This is the framework for the Expert's Tracing Assistant (ETA) system. For a representative set of nine structures in the Visible Human imagery, ETA was compared and contrasted to two state-of-the-art, user guided methods--Intelligent Scissors (IS) and Active Contour Models (ACM). Each method was used to define a boundary, and the distances between these boundaries and an expert's ground truth were compared. Across independent trials, there will be a natural variation in an expert's boundary tracing, and this degree of variation served as a benchmark against which these three methods were compared. For simple structural boundaries, all the methods were equivalent. However, in more difficult cases, ETA was shown to significantly better replicate the expert's boundary than either IS or ACM. In these cases, where the expert's judgement was most called into play to bound the structure, ACM and IS could not adapt to the boundary character used by the expert while ETA could

Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images

Background To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. Methods in the current work, entropy based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results the segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be as accurate when only trained by 8 GLCMs. Conclusion The research illustrated the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision

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