Stomach cancer is a type of cancer that is hard to detect at an early stage because it gives almost no symptoms at the beginning. Stomach cancer is an increasing incidence of cancer both in the World ...as well as in Turkey. The most common method used worldwide for gastric cancer diagnosis is endoscopy. However, definitive diagnosis is made with endoscopic biopsy results. Diagnosis with endoscopy is a very specific and sensitive method. With high-resolution endoscopy it is possible to detect mild discolorations, bulges and structural changes of the surface of the mucosa. However, because the procedures are performed with the eye of a doctor, it is possible that the cancerous areas may be missed and / or incompletely detected. Because of the fact that the cancerous area cannot be completely detected may cause the problem of cancer recurrence after a certain period of surgical intervention. In order to overcome this problem, a computerized decision support system (CDS) has been implemented with the help of specialist physicians and image processing techniques. The performed CDS system works as an assistant to doctors of gastroenterology, helping to identify the cancerous area in the endoscopic images of the scaffold, to take biopsies from these areas and to make a better diagnosis. We believe that gastric cancer will be helpful in determining the area and biopsy samples taken from the patient will be useful in determining the area. It is therefore considered a useful model.
This article proposes an efficient and adaptive statistical superpixel merging approach with edge penalty for polarimetric synthetic aperture radar (PolSAR) image segmentation. Based on the initial ...superpixel over-segmentation result obtained by our previously proposed adaptive polarimetric superpixel generation algorithm (Pol-ASLIC), this work achieves efficient and accurate PolSAR image segmentation by merging superpixels using the statistical region merging (SRM) framework. This article proposes to define a new dissimilarity measure between superpixels, which takes the edge penalty into consideration, leading to a reasonable and accurate merging order for superpixel pairs. With regard to the merging predicate of superpixels, a polarimetric homogeneity measurement (HoM) is used to define the merging threshold, making the merging predicate and merging threshold adaptive to the PolSAR image content. Experimental results on three airborne and one spaceborne PolSAR data sets demonstrate that the proposed approach can effectively improve the computation efficiency and segmentation accuracy in comparison with state-of-the-art merging-based methods for PolSAR data. More importantly, the proposed approach is free of parameters and easy to use.
This paper presents a new spectral-spatial classification method for hyperspectral (HS) images. The proposed method is based on integrating hierarchical segmentation results into Markov random field ...(MRF) spatial prior in the Bayesian framework. This work includes two main contributions. First, statistical region merging (SRM) segmentation algorithm is extended to a hierarchical version, HSRM. Second, a method for extracting a multilevel "fuzzy no-border/border" map from HSRM segmentation hierarchy is proposed, which are then exploited as weighting coefficients to modify the spatial prior of MRF-based multilevel logistic (MLL) model. The proposed method, named as MRF + HSRM, addresses the common problem of MRF-based methods, i.e., over-smoothing of classification result. Several experiments are conducted using real HS images to evaluate the performance of the proposed method in comparison with conventional MRF, and some state-of-the-art weighted MRF and object-based classifiers. To estimate the class conditional probability distribution in Bayesian framework, probabilistic support vector machine (SVM) and subspace multinomial logistic regression (MLRsub) classifiers are used. The experimental results demonstrate that the proposed method is able to generate more homogeneous regions similar to MRF-based methods, while preserving class boundaries as accurately as segmentation-based methods. The overhead computational burden of the proposed hierarchical segmentation stage is negligible considering the improvement it offers in classification results.
Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform ...tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps.
In this paper, we propose a fast Line Segment Detection algorithm for Polarimetric synthetic aperture radar (PolSAR) data (PLSD). We introduce the Constant False Alarm Rate (CFAR) edge detector to ...obtain the gradient map of the PolSAR image, which tests the equality of the covariance matrix using the test statistic in the complex Wishart distribution. A new filter configuration is applied here to save time. Then, the Statistical Region Merging (SRM) framework is utilized for the generation of line-support regions. As one of our main contributions, we propose a new Statistical Region Merging algorithm based on gradient Strength and Direction (SRMSD). It determines the merging predicate with consideration of both gradient strength and gradient direction. For the merging order, we set it by bucket sort based on the gradient strength. Furthermore, the pixels are restricted to belong to a unique region, making the algorithm linear in time cost. Finally, based on Markov chains and a contrario approach, the false alarm control of line segments is implemented. Moreover, a large scene airport detection method is designed based on the proposed line segment detection algorithm and scattering characteristics. The effectiveness and applicability of the two methods are demonstrated with PolSAR data provided by UAVSAR.
Background: As a result of advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest ...in the computer‐aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, because the accuracy of the subsequent steps crucially depends on it.
Methods: In this article, we present a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the statistical region merging algorithm.
Results: The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which three sets of dermatologist‐determined borders are used as the ground‐truth. The proposed method is compared with four state‐of‐the‐art automated methods (orientation‐sensitive fuzzy c‐means, dermatologist‐like tumor extraction algorithm, meanshift clustering, and the modified JSEG method).
Conclusion: The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images.
Among many types of efforts to improve the accuracy of remote sensing image classification, using spatial information is an effective strategy. The classification method integrates spatial ...information into spectral information, which is called the spectral-spatial classification approach, has better performance than traditional classification methods. Construct spectral-spatial distance used for classification is a common method to combine the spatial and spectral information. In order to improve the performance of spectral-spatial classification based on spectral-spatial distance, we introduce the information content (IC) in which two pixels are shared to measure spatial relation between them and propose a novel spectral-spatial distance measure method. The IC of two pixels shared was computed from the hierarchical tree constructed by the statistical region merging (SRM) segmentation. The distance we proposed was applied in two distance-based contextual classifiers, the k-nearest neighbors-statistical region merging (k-NN-SRM) and optimum-path forest-statistical region merging (OPF-SRM), to obtain two new contextual classifiers, the k-NN-SRM-IC and OPF-SRM-IC. The classifiers with the novel distance were implemented in four land cover images. The classification results of the classifier based on our spectral-spatial distance outperformed all the other competitive contextual classifiers, which demonstrated the validity of the proposed distance measure method.
The paper presents the results of a study to define the current size and location of workplaces and business entities in the area. The research was conducted in three stages. In the first step, the ...analysis of the size and distribution of workplaces according to the municipalities in the Republic of Slovenia for the 2007-2019 period was carried out. In the second step, the size and distribution of business entities in the space were examined. In the third step, a more detailed analysis was carried out in the test area of the Osrednjeslovenska statistical region, based on: the location in Ljubljana Urban Municipality (hereinafter referred to as MOL), in the municipalities directly bordering MOL and in the other municipalities of the Osrednjeslovenska statistical region. At this stage we also focused on the sector of activity of business entities. A strong upward trend in workplaces was found in only a few major urban centres along the motorway junction, especially in MOL. The same applies to the concentration of business entities. All this suggests that the existing practice of planning activities in physical space does not follow the strategic orientations of spatial acts, which could better control the spatial processes and their consequences.
The statistical region merging (SRM) algorithm exhibits efficient performance in solving significant noise corruption and does not depend on the data distribution. These advantages make SRM suitable ...for the segmentation of synthetic aperture radar (SAR) images, which are characterized by speckle noise and different distributions of various data types and spatial resolutions. However, the original SRM algorithm is designed for RGB and gray images characterized by additive noise and having a range of 0, 255. In this letter, the SRM algorithm is generalized so that it can be applied to images with larger range and multiplicative noise. The original 4-neighborhood models are also generalized into 8-neighborhood models. The effectiveness of the generalized SRM (GSRM) algorithm is demonstrated by AirSAR and ESAR L-band Polarimetric SAR (PolSAR) data. Given that the input data of the GSRM algorithm can be single- or multi-dimensional, the proposed GSRM algorithm can be used for single- and multi-polarized as well as for fully polarimetric SAR data.
•An automated vessel tree segmentation method in angiography images is presented.•An adaptive Hessian-based enhancement method improves segmentation performance.•A statistical region merging ...technique segments both principal and thin vessels.
Coronary artery segmentation is a fundamental step for a computer-aided diagnosis system to be developed to assist cardiothoracic radiologists in detecting coronary artery diseases. Manual delineation of the vasculature becomes tedious or even impossible with a large number of images acquired in the daily life clinic. A new computerized image-based segmentation method is presented for automatically extracting coronary arteries from angiography images.
A combination of a multiscale-based adaptive Hessian-based enhancement method and a statistical region merging technique provides a simple and effective way to improve the complex vessel structures as well as thin vessel delineation which often missed by other segmentation methods. The methodology was validated on 100 patients who underwent diagnostic coronary angiography. The segmentation performance was assessed via both qualitative and quantitative evaluations.
Quantitative evaluation shows that our method is able to identify coronary artery trees with an accuracy of 93% and outperforms other segmentation methods in terms of two widely used segmentation metrics of mean absolute difference and dice similarity coefficient.
The comparison to the manual segmentations from three human observers suggests that the presented automated segmentation method is potential to be used in an image-based computerized analysis system for early detection of coronary artery disease.
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