Watershed Image Segmentation Algorithm Research Articles

A method of depth extension in 3D shape measurement by geometric constraints

In this paper a new method to extend the measurement depth limited by geometric constraints for objects with large depth variances is presented. This method mainly includes the following steps: (1) using the watershed image segmentation algorithm to extract the correct part of the discontinuous absolute phase distribution obtained by geometric constraints, and making a binary mask of the correspondent region; (2) using the window Fourier filter-quality guided phase unwrapping algorithm to obtain the relative phase distribution of the tested object; (3) using the binary mask to get the difference between the relative and the absolute phases; (4) converting the relative phase distribution to absolute phase distribution by referring to their phase difference in the mask region. The experimental results prove the effectiveness and correctness of the proposed method.

Pore system, microstructure and porosity characterization of Gondwana shale of Eastern India using laboratory experiment and watershed image segmentation algorithm

Abstract Porosity is the most crucial parameter to assess the potential of fine grain rocks like shale. Shale has very low connected porosity and permeability, which controls the fluid flow and migration. Therefore, detection of connected and closed pore network in digital images can help to evaluate porosity and permeability of rock. The objective of this paper is to characterize Gondwana shale of eastern India in micro-scale, that is, in terms of its porosity, pore-structure and pore size distribution for its shale gas potential. Watershed-transform has been performed to segment pores, throat and mineral grains by using 2-D Scan Electron Microscopy (SEM) images of Gondwana shale of Barren-Measures Formation. The watershed transform, when coupled with morphological operators and a customized disk shape structuring element, segments the pores and throats efficiently and estimates the porosity of Gondwana shale for the very first time. Three Gondwana shale samples from different borehole depth is used for this study. Finally, porosity measured in a laboratory using Mercury Injection Capillary Pressure (MICP) test, is compared with the numerically modeled watershed porosity values, which shows good agreement between the two sets of results with precisely segmented pore bodies and throat bodies at appropriate locations. Most of the pores in the sample are found to be in a mesopore category (2–50 nm). The porosity values measured using MICP ranges from 5.01% to 6.53% whereas the porosity estimated from watershed image segmentation ranges from 5.21% to 6.91%.

An Improved Image Inpainting Algorithm Based on Image Segmentation

At present, the image restoration algorithm based on sample block is not ideal for image processing with abundant texture information, and it is easy to produce false matching texture blocks in the process of repairing. An improved image inpainting algorithm based on image segmentation is proposed in this paper to address this problem. Firstly, the watershed image segmentation algorithm is used for image segmentation, which is combined with curvature feature of isophotes to repair the image. Curvature features reflecting the details of texture character are introduced in the data items to calculate the priority. Secondly, the matching of the pixel block must be searched in the image segmentation region, which can be a better solution to avoid the phenomenon of excessive extension of the texture block in the process of image restoration. Compared with the original Criminisi algorithm and other improved algorithms, the experimental result shows that the method we proposed have a larger PSNR value of image, which means to have better repair effects on the image including abundant texture information.

A Novel Watershed Image Segmentation Algorithm Based on Quantum Inspired Morphology

A Novel Watershed Image Segmentation Algorithm Based on Quantum Inspired Morphology

영역분할과 컬러 특징을 이용한 건물 인식기법

This paper proposes a building recognition algorithm using watershed image segmentation algorithm and integrated region matching (IRM). To recognize a building, a preprocessing algorithm which is using Gaussian filter to remove noise and using canny edge extraction algorithm to extract edges is applied to input building image. First, images are segmented by watershed algorithm. Next, a region adjacency graph (RAG) based on the information of segmented regions is created. And then similar and small regions are merged. Second, a color distribution feature of each region is extracted. Finally, similar building images are obtained and ranked. The building recognition algorithm was evaluated by experiment. It is verified that the result from the proposed method is superior to color histogram matching based results.

A Modified Watershed Segmentation Algorithm using Distances Transform for Image Segmentation

In this paper, we propose a modified watershed algorithm for image segmentation using distances transform and image smoothing method, an improved version of watershed segmentation. This algorithm allows better boundary localization due to the edge information brought by watersheds. Thus, the proposed method has been found to be able to reduce over segmentation and this would ultimately lead to easier handling by the machine towards higher level of processing at subsequent stages. The algorithm has been tested on colored image obtained from real life and has been found to yield satisfactory segmentation results.

A Modified Watershed Algorithm for Stellar Image

A modified gray scale watershed image segmentation algorithm suitable for low contrast image has been proposed. Digital images acquired from far away stellar objects (like stars, planets, galaxies, comets etc.) are prone to be severally affected by various types of noises and the contrast of these categories of images are generally found to be low. In present study, a preserving de noising method is presented by a contrast adjustment based on adaptive histogram equalization technique. The proposed method has been found to yield satisfactory segmentation of the stellar images. The entropy of the original and the segmented image is compared and the result confirms to the reality.

Liver Image Segmentation Using Improved Watershed Method

This paper introduces an improved watershed algorithm for liver image segmentation. Medical images have complicated structure and the soft tissues have deformation sometimes. To exactly conduct the following image registration or surgery navigation, the image segmentation must identify the changes quickly and accurately. Watershed algorithm has fast speed and good edge location for complex structure, but it is sensitive to noise and has the over-segmentation problem. In this paper, pre-processing and post-processing methods are proposed during watershed segmentation procedure. According to the thresholds of region area and gray difference between adjacent regions, the image noise is reduced at pre-processing stage and the over-segmented regions are merged at post-processing part. Through the experiment of two similar liver images, we can see the segmented images have clear outline and the difference of two images can be identified obviously.

Automated three-dimensional microbial sensing and recognition using digital holography and statistical sampling.

We overview an approach to providing automated three-dimensional (3D) sensing and recognition of biological micro/nanoorganisms integrating Gabor digital holographic microscopy and statistical sampling methods. For 3D data acquisition of biological specimens, a coherent beam propagates through the specimen and its transversely and longitudinally magnified diffraction pattern observed by the microscope objective is optically recorded with an image sensor array interfaced with a computer. 3D visualization of the biological specimen from the magnified diffraction pattern is accomplished by using the computational Fresnel propagation algorithm. For 3D recognition of the biological specimen, a watershed image segmentation algorithm is applied to automatically remove the unnecessary background parts in the reconstructed holographic image. Statistical estimation and inference algorithms are developed to the automatically segmented holographic image. Overviews of preliminary experimental results illustrate how the holographic image reconstructed from the Gabor digital hologram of biological specimen contains important information for microbial recognition.

An improved fast watershed algorithm for image segmentation

Connected component based on chain codes is a simple and effective method to implement watersheds based on rainfall simulation. However, time complexity of existing algorithm based on chain code remained high as it scans the image four times. In this paper, we propose an improved fast watershed algorithm, which scans the image only twice, one for generating connected components and other for labelling the catchment basins. Experimental results show that proposed algorithm produces expected results twice faster than existing algorithm.

CASE-BASED-REASONING FOR IMAGE SEGMENTATION

This paper proposes to use case-based-reasoning for grey-level image segmentation. Different approaches to image segmentation have been proposed in the literature. The selection of the segmentation approach and the assignment of the values to the parameters involved in the selected algorithm depend on image domain and on the specific application. Case-based-reasoning seems a promising way to make the above selection automatic. In this paper, we describe the results of a preliminary study done in this respect. In particular, we refer to the automatic selection of the values of the parameters for a new watershed image segmentation algorithm.

Improved order-invariant watershed algorithm for image segmentation

Creative lake minimum was introduced to represent topographical information of a pixel,so as to reduce the number of RIDGE labels in order-invariant watershed algorithm.Watershed fall was used to reduce the number of over-segmented regions.This algorithm was implemented in both immersion and toboggan ways.Experiment results show that the proposed algorithm is still order-invariant,and it can reduce the number of RIDGE labels by about 80% as well as segmented regions by 5%~20%.

A three-object model for the similarity searches of chest CT images

We propose in this paper a three-object model specifically for the archiving and retrieval of chest CT images. To calculate parameters for the model, each chest CT image needs to be processed to segment the three main objects and then the features be extracted to describe the objects' properties and relationships. In the image segmentation part, we applied the knowledge of the modality on chest CT images and modified the traditional watershed image segmentation algorithm including a four-step merging algorithm specifically for chest CT images. After segmentation, the mediastinum and two lung lobes are identified. The mediastinum object is mainly described by shape-related features while the two lung lobes are described mainly by texture features. A three-object model was exploited to describe the object features and the spatial relationship among objects. To test the capability of the three-object model to the similarity searches of chest CT images, we developed a CBIR system in which three distinct query modes were provided. They are 'searching by ARGs', 'searching by shape features of mediastinum', and 'searching by texture features of lung lobes'. The experimental results show that the three-object model demonstrates impressive power in the similarity searching of chest CT images. Among the three searching modes, the 'searching by shape features of mediastinum' and 'searching by texture features of lung lobes' modes provide user choices to search for images with high similarities in specific objects rather than in the whole images. The precision rate of either query mode is high, with an average of around 80% out of the first 30 result images are justified as similar, which is impressive in a fully automatic image query system using content features. Nevertheless, the two query modes that concentrate on distinct object features show slightly better capability in searching for similar images than the 'searching by ARGs' mode.

An Improved Parallel Watershed Algorithm for Image Segmentation

An Improved Parallel Watershed Algorithm for Image Segmentation