Active Contours and Image Segmentation: The Current State Of the Art

Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours

Spring-Charged Particles Model to Improved Shape Recovery:An Application for X-Ray Spinal Segmentation

Deformable models are widely used in medical image segmentation methods, to find not only single but also multiple objects within an image. They have the ability to follow the contours of an object of interest, define the boundary of ROI (Region Of Interest) and improve shape recovery. However, these methods still have limitations in cases of low image quality or clutter. This paper presents a new deformable model, the Spring-Charged Particles Model (SCPM). It simulates the movement of positively charged particles connected by springs, attracted towards the contour of objects of interest which is charged negatively, according to the gradient-magnitude image. Springs prevent the particles from moving away and keep the particles at appropriate distances without reducing their flexibility. SCPM was tested on simple shape images and on frontal X-ray images of scoliosis patients. Artificial noise was added to the simple images to examine the robustness of the method. Several configurations of springs and positively charged-particles were evaluated by determining the best spinal segmentation result. The performance of SCPM was compared to the Charged Fluid Model (CFM), Active Contours, and a convolutional neural network (CNN) with U-Net architecture to measure its ability for determining the curvature of the spinal column from frontal X-Ray images. The results show that SCPM is better at segmenting the spine and determining its curvature, as indicated by the highest Area Score value of 0.837, and the lowest standard deviation value of 0.028

Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.

We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects

Focus+Context via Snaking Paths

Focus+context visualizations reveal specific structures in high detail while effectively depicting its surroundings, often relying on transitions between the two areas to provide context. We present an approach to generate focus+context visualizations depicting cylindrical structures along snaking paths that enables the structures themselves to become the transitions and focal areas, simultaneously. A method to automatically create a snaking path through space by applying a path finding algorithm is presented. A 3D curve is created based on the 2D snaking path. We describe a process to deform cylindrical structures in segmented volumetric models to match the curve and provide preliminary geometric models as templates for artists to build upon. Structures are discovered using our constrained volumetric sculpting method that enables removal of occluding material while leaving them intact. We find the resulting visualizations effectively mimic a set of motivating illustrations and discuss some limitations of the automatic approach

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

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