DNA microarray image segmentation using active contours without edges method

The goal of this dissertation is to build a better segmentation method for DNA microarray image processing. Segmentation is a partitioning process used to separate a spot area from a non-spot area in DNA microarrays. It directly affects the accuracy of gene expression analysis in the data mining process that follows. A number of DNA microarray segmentation methods have been proposed in the area, but even modern segmentation methods seem to have accuracy problems. In this dissertation, I will present a segmentation method based on the Active Contours Without Edges (ACWE) algorithm and apply it to two types of DNA microarrays, complementary DNA (cDNA) and Affymetrix GeneChip. Several adjustments will be applied to the original ACWE method to use it more efficiently in the microarray processing area. As a secondary research objective, I will improve the ACWE method by using higher order schemes in finite difference method for solving the partial differential equation (PDE). The original ACWE method used the associated Euler-Lagrange partial differential equation for the Lipschitz function Φ. It used the lower order finite difference schemes to solve the PDE. The improved ACWE method defines the higher order finite difference schemes to increase the accuracy of segmentation. Various experimental results will be presented to show that the ACWE method is more efficient than other DNA microarray image segmentation methods. Statistical analysis is performed to compare the newly proposed method with the previously best methods in this area. Experimental results will also be presented to show that the improved ACWE method has more accurate segmentation results than the ACWE method

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

Active contours driven by local and global intensity fitting energy with application to SAR image segmentation and its fast solvers

In this paper, we propose a novel variational active contour model based on Aubert-Aujol (AA) denoising model, which hybrides geodesic active contour (GAC) model with active contours without edges (ACWE) model and can be used to segment images corrupted by multiplicative gamma noise. We transform the proposed model into classic ROF model by adding a proximity term. Inspired by a fast denosing algorithm proposed by Jia-Zhao recently, we propose two fast fixed point algorithms to solve SAR image segmentation question. Experimental results for real SAR images show that the proposed image segmentation model can efficiently stop the contours at weak or blurred edges, and can automatically detect the exterior and interior boundaries of images with multiplicative gamma noise. The proposed fast fixed point algorithms are robustness to initialization contour, and can further reduce about 15% of the time needed for algorithm proposed by Goldstein-Osher.Comment: 20 pages,28 figures. arXiv admin note: substantial text overlap with arXiv:2312.08376, arXiv:2312.0936

Multidirectional Building Detection in Aerial Images Without Shape Templates

Abstract. The aim of this paper is to exploit orientation information of an urban area for extracting building contours without shape templates. Unlike using shape templates, these given contours describe more variability and reveal the fine details of the building outlines, resulting in a more accurate detection process, which is beneficial for many tasks, like map updating and city planning. According to our assumption, orientation of the closely located buildings is coherent, it is related to the road network, therefore adaptation of this information can lead to more efficient building detection results. The introduced method first extracts feature points for representing the urban area. Orientation information in the feature point neighborhoods is analyzed to define main orientations. Based on orientation information, the urban area is classified into different directional clusters. The edges of the classified building groups are then emphasized with shearlet based edge detection method, which is able to detect edges only in the main directions, resulting in an efficient connectivity map. In the last step, with the fusion of the feature points and connectivity map, building contours are detected with a non-parametric active contour method. </jats:p

Quantifying the Consistency and Characterizing the Confidence of Coronal Holes Detected by Active Contours without Edges (ACWE)

This paper presents an intramethod ensemble for coronal hole (CH) detection based on the Active Contours Without Edges (ACWE) segmentation algorithm. The purpose of this ensemble is to develop a confidence map that defines, for all on disk regions of a Solar extreme ultraviolet (EUV) image, the likelihood that each region belongs to a CH based on that region's proximity to, and homogeneity with, the core of identified CH regions. CHs are regions of open magnetic field lines, resulting in high speed solar wind. Accurate detection of CHs is vital for space weather prediction. By relying on region homogeneity, and not intensity (which can vary due to various factors including line of sight changes and stray light from nearby bright regions), to define the final confidence of any given region, this ensemble is able to provide robust, consistent delineations of the CH regions. Using the metrics of global consistency error (GCE), local consistency error (LCE), intersection over union (IOU), and the structural similarity index measure (SSIM), the method is shown to be robust to different spatial resolutions and different intensity resolutions. Furthermore, using the same metrics, the method is shown to be robust across short timescales, indicating self-consistent segmentations. Finally, the accuracy of the segmentations and confidence maps are validated by considering the skewness (i.e., unipolarity) of the underlying magnetic field

A locally statistical active contour model for SAR image segmentation can be solved by denoising algorithms

In this paper, we propose a novel locally statistical variational active contour model based on I-divergence-TV denoising model, which hybrides geodesic active contour (GAC) model with active contours without edges (ACWE) model, and can be used to segment images corrupted by multiplicative gamma noise. By adding a diffusion term into the level set evolution (LSE) equation of the proposed model, we construct a reaction-diffusion (RD) equation, which can gradually regularize the level set function (LSF) to be piecewise constant in each segment domain and gain the stable solution. We further transform the proposed model into classic ROF model by adding a proximity term. Inspired by a fast denoising algorithm proposed by Jia-Zhao recently, we propose two fast fixed point algorithms to solve SAR image segmentation question. Experimental results for real SAR images show that the proposed image segmentation model can efficiently stop the contours at weak or blurred edges, and can automatically detect the exterior and interior boundaries of images with multiplicative gamma noise. The proposed FPRD1/FPRD2 models are about 1/2 (or less than) of the time required for the SBRD model based on the Split Bregman technique.Comment: 18 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:2312.11849, arXiv:2312.08376, arXiv:2312.0936

An Approach for Efficient Detection of Cephalometric Landmarks

AbstractIn this paper, a method is developed for the automated identification of cephalometric landmarks in orthodontics. The process of soft tissue edge detection is divided into two steps: detecting the sub-images that contained the required landmarks using combination of the Histograms of Oriented Gradients (HOG) descriptor with the Support Vector Machine (SVM), then utilizing Thresholding and Mathematical Morphological (TMM) algorithm to trace soft tissue profile. In addition, the mandible's edge is detected by the Active contours without edges (Chan-Vese method). Finally, the landmarks of soft tissue profile and the mandible's edge are pinned based on analyzing the contour plot of these lines. The simulation results have high accuracy

Robust active contour segmentation with an efficient global optimizer

Active contours or snakes are widely used for segmentation and tracking. Recently a new active contour model was proposed, combining edge and region information. The method has a convex energy function, thus becoming invariant to the initialization of the active contour. This method is promising, but has no regularization term. Therefore segmentation results of this method are highly dependent of the quality of the images. We propose a new active contour model which also uses region and edge information, but which has an extra regularization term. This work provides an efficient optimization scheme based on Split Bregman for the proposed active contour method. It is experimentally shown that the proposed method has significant better results in the presence of noise and clutter