Ran Su

Junction detection for linear structures based on Hessian, correlation and shape information

Junctions have been demonstrated to be important features in many visual tasks such as image registration, matching, and segmentation, as they can provide reliable local information. This paper presents a method for detecting junctions in 2D images with linear structures as well as providing the number of branches and branch orientations. The candidate junction points are selected through a new measurement which combines Hessian information and correlation matrix. Then the locations of the junction centers are refined and the branches of the junctions are found using the intensity information of a stick-shaped window at a number of orientations and the correlation value between the intensity of a local region and a Gaussian-shaped multi-scale stick template. The multi-scale template is used here to detect the structures with various widths. We present the results of our algorithm on images of different types and compare our algorithm with three other methods. The results have shown that the proposed approach can detect junctions more accurately.

A new method for linear feature and junction enhancement in 2D images based on morphological operation, oriented anisotropic Gaussian function and Hessian information

Abstract Feature enhancement is an important preprocessing step in many image processing tasks. It is the process of adjusting image intensities so that the enhanced results are more suitable for analysis. Good enhancement results for linear structures such as vessels or neurites can be used as inputs for segmentation and other operations. In this paper, a novel linear feature enhancement filter – an adaptive multi-scale morpho-Gaussian filter – which can enhance and smooth linear features is proposed based on morphological operation, anisotropic Gaussian function and Hessian information. This filter can enhance and smooth along the local orientation of the linear structures and the Hessian measurement is used to further enhance the linear features. We utilize the Hessian matrix to calculate the orientation information for our directional morphological operation and the oriented anisotropic Gaussian smoothing. We also propose a novel method for junction enhancement, which can solve the problem of junction suppression. We decompose the junctions and enhance along each linear structure within a junction region. We present the test results of our algorithm on images of different types and compare our method with three existing methods. The experimental results show that the proposed approach can achieve better results.

Segmentation of clustered nuclei based on curvature weighting

Cluster of nuclei are frequently observed in thick tissue section images. It is very important to segment overlapping nuclei in many biomedical applications. Many existing methods tend to produce under segmented results when there is a high overlap rate. In this paper, we present a curvature weighting based algorithm which weights each pixel using the curvature information of its nearby boundaries to extract markers, each of which represents an object, from input images. Then we use marker-controlled watershed to obtain the final segmentation. Test results using both synthetic and real cell images are presented in the paper.

Clustered nuclei splitting via curvature information and gray-scale distance transform

Clusters or clumps of cells or nuclei are frequently observed in two dimensional images of thick tissue sections. Correct and accurate segmentation of overlapping cells and nuclei is important for many biological and biomedical applications. Many existing algorithms split clumps through the binarization of the input images; therefore, the intensity information of the original image is lost during this process. In this paper, we present a curvature information, gray scale distance transform, and shortest path splitting line‐based algorithm which can make full use of the concavity and image intensity information to find out markers, each of which represents an individual object, and detect accurate splitting lines between objects using shortest path and junction adjustment. The proposed algorithm is tested on both synthetic and real nuclei images. Experiment results show that the performance of the proposed method is better than that of marker‐controlled watershed method and ellipse fitting method.

Linear feature enhancement based on morphological operation and Gabor function

In many image processing tasks, feature enhancement is an important preprocessing step. It is the process of adjusting images so that the results are more suitable for analysis. Good enhancement results of linear features such as vessels or neurites can be inputs for segmentation or tracking. In this paper, we propose a novel linear feature enhancement method based on morphological operation and Gabor function, which can enhance and smooth linear features. We use and improve the Hessian matrix to calculate the orientation information for our directional morphological operation and Gabor smoothing. An approach for junction enhancement in each branch is also proposed here. We present the results of our algorithm on images of different types. The obtained outputs show that the proposed approach can achieve very good results.

A novel method for dendritic spines detection based on directional morphological filter and shortest path

Dendritic spines are tiny membranous protrusions from neurons dendrites. They play a very important role in the nervous system. A number of mental diseases such as Alzheimers disease and mental retardation are revealed to have close relations with spine morphologies or spine number changes. Spines have various shapes, and spine images are often not of good quality; hence it is very challenging to detect spines in neuron images. This paper presents a novel pipeline to detect dendritic spines in 2D maximum intensity projection (MIP) images and a new dendrite backbone extraction method is developed in the pipeline. The strategy for the backbone extraction approach is that it iteratively refines the extraction result based on directional morphological filtering and improved Hessian filtering until a satisfactory extraction result is obtained. A shortest path method is applied along a backbone to extract the boundary of the dendrites. Spines are then segmented from the dendrites outside the extracted boundary. Touching spines will be split using a marker-controlled watershed algorithm. We present the results of our algorithm on real images and compare our algorithm with two other spine detection methods. The results show that the proposed approach can detect dendrites and spines more accurately. Measurements and classification of spines are also made in this paper.

Dendritic spines detection based on directional morphological filter and shortest path

Dendritic spines are membranous protrusions from neurons dendrites. They play a very important role in the nervous system. They are very small and have various shapes; hence it is very challenging to detect them in neuron images. This paper presents a novel method for detecting dendritc spines in 2D images. A new dendrite backbone or centerline extraction method is introduced herein which is based on an iterative process between smoothing and extraction. The proposed method iteratively refines the extraction result using both directional morphological filtering and improved Hessian filtering until a satisfactory extraction result is obtained. A shortest path method is applied along a backbone to extract the boundary of the dendrites. Spines are then segmented from the dendrites outside the extracted boundary. The proposed algorithm has been tested with many images and good results are achieved.

Tubule detection in testis images using boundary weighting and circular shortest path

In studies of germ cell transplantation, measureing tubule diameters and counting cells from different populations using antibodies as markers are very important. Manual measurement of tubule sizes and cell counts is a tedious and sanity grinding work. In this paper, we propose a new boundary weighting based tubule detection method. We first enhance the linear features of the input image and detect the approximate centers of tubules. Next, a boundary weighting transform is applied to the polar transformed image of each tubule region and a circular shortest path is used for the boundary detection. Then, ellipse fitting is carried out for tubule selection and measurement. The algorithm has been tested on a dataset consisting of 20 images, each having about 20 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually.

Junction Detection for Linear Structures

This paper presents a method for detecting junctions in an image with linear structures. The candidate junction points are selected through the combination of correlation matrix and Hessian information; then the branches of the junctions are found according to the intensity information and the correlation value between intensity profile of cross sections and a Gaussian‐shaped template. Junction detection results for neurite images are provided.