Improved techniques for automatic image segmentation

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Automatic Spinal Cord Segmentation From Medical MR Images using Hybrid Algorithms

Medical image processing is the top most research area. There are huge amount of researches found in the medical image analyze, classification and segmentation process. Spinal cord segmentation of MRI images is the challenging process. In the proposed research work, automatic Spinal Cord (SC) segmentation from medical MRI image is performed with various techniques. The proposed work improves the segmentation with less iteration and improved accuracy by adopting improved Weighted Expectation Maximization (WEM) and Strong Fitness Firefly (SFF) algorithms. The proposed work effectively segments the spinal cord by applying effective pre-processing, image enhancement process and clustering with less iterations. Using the combination of different techniques, the proposed system effectively identifies the spinal cord from the MRI image, the experiments performed using Matlab tool. The accuracy is calculated and shown for the proposed system. The result shows, the mixed approach of WEM and SFF increases the segmentation accuracy than using the WEM alone

Contrast enhancement for improved blood vessels retinal segmentation using top-hat transformation and otsu thresholding

Diabetic Retinopathy is a effect of diabetes. It results abnormalities in the retinal blood vessels. The abnormalities can cause blurry vision and blindness. Automatic retinal blood vessels segmentation on retinal image can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques. In image enhancement, it combined CLAHE with Top-hat transformation to improve image quality. The study used DRIVE dataset that provided retinal image data. The image data in dataset was generated by the fundus camera. The CLAHE and Top-hat transformation methods were applied to rise the contrast and reduce noise on the image. The images that had good quality could help the segmentation process to find blood vessels in retinal images appropriately by a computer. It improved the performance of the segmentation method for detecting blood vessels in retinal image. Otsu Thresholding was used to segment blood vessel pixels and other pixels as background by local threshold. To evaluation performance of the proposed method, the study has been measured accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the averages of accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively. It indicated that the proposed method was successful and well to work on blood vessels segmentation retinal images especially for thick blood vessels

Automatic Optimum Atlas Selection for Multi-Atlas Image Segmentation using Joint Label Fusion

Multi-atlas image segmentation using label fusion is one of the most accurate state of the art image segmentation techniques available for biomedical imaging applications. Motivated to achieve higher image segmentation accuracy, reduce computational costs and a continuously increasing atlas data size, a robust framework for optimum selection of atlases for label fusion is vital. Although believed not to be critical for weighted label fusion techniques by some works (Sabuncu, M. R. et al., 2010, [1]), others have shown that appropriate atlas selection has several merits and can improve multi-atlas image segmentation accuracy (Aljabar et al., 2009, [2], Van de Velde et al., 2016) [27]. This thesis proposed an automatic Optimum Atlas Selection (OAS) framework pre-label fusion step that improved image segmentation performance dice similarity scores using Joint Label Fusion (JLF) implementation by Wang et al, 2013, [3, 26]. A selection criterion based on a global majority voting fusion output image similarity comparison score was employed to select an optimum number of atlases out of all available atlases to perform the label fusion step. The OAS framework led to observed significant improvement in aphasia stroke heads magnetic resonance (MR) images segmentation accuracy in leave-one out validation tests by 1.79% (p = 0.005520) and 0.5% (p = 0.000656) utilizing a set of 7 homogenous stroke and 19 inhomogeneous atlas datasets respectively. Further, using comparatively limited atlas data size (19 atlases) composed of normal and stroke head MR images, t-tests showed no statistical significant difference in image segmentation performance dice scores using the proposed OAS protocol compared to using known automatic Statistical Parametric Mapping (SPM) plus a touchup algorithm protocol [4] for image segmentation (p = 0.49417). Thus, leading to the conclusions that the proposed OAS framework is an effective and suitable atlas selection protocol for multi-atlas image segmentation that improves brain MR image segmentation accuracy. It is comparably in performance to known image segmentation algorithms and can lead to reduced computation costs in large atlas data sets. With regards to future work, efforts to increase atlas data size and use of a more robust approach for determining the optimum selection threshold value and corresponding number of atlases to perform label fusion process can be explored to enhance overall image segmentation accuracy. Furthermore, for an unbiased performance comparison of the proposed OAS framework to other image segmentation algorithms, truly manually segmented atlas ground truth MR images and labels are needed

A CAD System for the Detection of Clustered Microcalcification in Digitized Mammogram Film

Cluster of microcalcification in mammograms are an important early sign of breast cancer. This report presents a computer aided diagnosis (CAD) system for the automatic detection of cluster rnicrocalcifications in digitized mammograms. The main objective of this study is to present the approach for microcalcifications detection in mammography image. In literature review author illustrate the techniques used in image processing, segmentation, feature extraction and neural network in detecting rnicrocalcification. The proposed system consists of two main steps. First step is image preprocessing and segmentation in order to improve and enhance the quality of image. Then second step is feature extraction to analyze the image and conclude whether the case is malignant or benign. The programming of the project using MATLAB still need to be improved since it produce the output that did not meet the author expectation especially in feature extraction

Scene segmentation using temporal clustering for accessing and re-using broadcast video

Scene detection is a fundamental tool for allowing effective video browsing and re-using. In this paper we present a model that automatically divides videos into coherent scenes, which is based on a novel combination of local image descriptors and temporal clustering techniques. Experiments are performed to demonstrate the effectiveness of our approach, by comparing our algorithm against two recent proposals for automatic scene segmentation. We also propose improved performance measures that aim to reduce the gap between numerical evaluation and expected results

Accurate cell segmentation in microscopy images using membrane patterns

Motivation: Identifying cells in an image (cell segmentation) is essential for quantitative single-cell biology via optical microscopy. Although a plethora of segmentation methods exists, accurate segmentation is challenging and usually requires problem-specific tailoring of algorithms. In addition, most current segmentation algorithms rely on a few basic approaches that use the gradient field of the image to detect cell boundaries. However, many microscopy protocols can generate images with characteristic intensity profiles at the cell membrane. This has not yet been algorithmically exploited to establish more general segmentation methods. Results: We present an automatic cell segmentation method that decodes the information across the cell membrane and guarantees optimal detection of the cell boundaries on a per-cell basis. Graph cuts account for the information of the cell boundaries through directional cross-correlations, and they automatically incorporate spatial constraints. The method accurately segments images of various cell types grown in dense cultures that are acquired with different microscopy techniques. In quantitative benchmarks and comparisons with established methods on synthetic and real images, we demonstrate significantly improved segmentation performance despite cell-shape irregularity, cell-to-cell variability and image noise. As a proof of concept, we monitor the internalization of green fluorescent protein-tagged plasma membrane transporters in single yeast cells. Availability and implementation: Matlab code and examples are available at http://www.csb.ethz.ch/tools/cellSegmPackage.zip. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Feature-sensitive and Adaptive Image Triangulation: A Super-pixel-based Scheme for Image Segmentation and Mesh Generation

With increasing utilization of various imaging techniques (such as CT, MRI and PET) in medical fields, it is often in great need to computationally extract the boundaries of objects of interest, a process commonly known as image segmentation. While numerous approaches have been proposed in literature on automatic/semi-automatic image segmentation, most of these approaches are based on image pixels. The number of pixels in an image can be huge, especially for 3D imaging volumes, which renders the pixel-based image segmentation process inevitably slow. On the other hand, 3D mesh generation from imaging data has become important not only for visualization and quantification but more critically for finite element based numerical simulation. Traditionally image-based mesh generation follows such a procedure as: (1) image boundary segmentation, (2) surface mesh generation from segmented boundaries, and (3) volumetric (e.g., tetrahedral) mesh generation from surface meshes. These three majors steps have been commonly treated as separate algorithms/steps and hence image information, once segmented, is not considered any more in mesh generation. In this thesis, we investigate a super-pixel based scheme that integrates both image segmentation and mesh generation into a single method, making mesh generation truly an image-incorporated approach. Our method, called image content-aware mesh generation, consists of several main steps. First, we generate a set of feature-sensitive, and adaptively distributed points from 2D grayscale images or 3D volumes. A novel image edge enhancement method via randomized shortest paths is introduced to be an optional choice to generate the features’ boundary map in mesh node generation step. Second, a Delaunay-triangulation generator (2D) or tetrahedral mesh generator (3D) is then utilized to generate a 2D triangulation or 3D tetrahedral mesh. The generated triangulation (or tetrahedralization) provides an adaptive partitioning of a given image (or volume). Each cluster of pixels within a triangle (or voxels within a tetrahedron) is called a super-pixel, which forms one of the nodes of a graph and adjacent super-pixels give an edge of the graph. A graph-cut method is then applied to the graph to define the boundary between two subsets of the graph, resulting in good boundary segmentations with high quality meshes. Thanks to the significantly reduced number of elements (super-pixels) as compared to that of pixels in an image, the super-pixel based segmentation method has tremendously improved the segmentation speed, making it feasible for real-time feature detection. In addition, the incorporation of image segmentation into mesh generation makes the generated mesh well adapted to image features, a desired property known as feature-preserving mesh generation