A Run-Based Two-Scan Labeling Algorithm Lifeng He; Yuyan Chao; Suzuki, K.
IEEE transactions on image processing,
05/2008, Letnik:
17, Številka:
5
Journal Article
Recenzirano
We present an efficient run-based two-scan algorithm for labeling connected components in a binary image. Unlike conventional label-equivalence-based algorithms, which resolve label equivalences ...between provisional labels, our algorithm resolves label equivalences between provisional label sets. At any time, all provisional labels that are assigned to a connected component are combined in a set, and the smallest label is used as the representative label. The corresponding relation of a provisional label and its representative label is recorded in a table. Whenever different connected components are found to be connected, all provisional label sets concerned with these connected components are merged together, and the smallest provisional label is taken as the representative label. When the first scan is finished, all provisional labels that were assigned to each connected component in the given image will have a unique representative label. During the second scan, we need only to replace each provisional label by its representative label. Experimental results on various types of images demonstrate that our algorithm outperforms all conventional labeling algorithms.
•Connected-component labeling (CCL) is indispensable for pattern recognition.•Many connected-component labeling algorithms have been proposed.•The state-of-the-art CCL algorithms presented in the ...last decade are reviewed.
This article addresses the connected-component labeling problem which consists in assigning a unique label to all pixels of each connected component (i.e., each object) in a binary image. Connected-component labeling is indispensable for distinguishing different objects in a binary image, and prerequisite for image analysis and object recognition in the image. Therefore, connected-component labeling is one of the most important processes for image analysis, image understanding, pattern recognition, and computer vision. In this article, we review state-of-the-art connected-component labeling algorithms presented in the last decade, explain the main strategies and algorithms, present their pseudo codes, and give experimental results in order to bring order of the algorithms. Moreover, we will also discuss parallel implementation and hardware implementation of connected-component labeling algorithms, extension for n-D images, and try to indicate future work on the connected component labeling problem.
As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image ...segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbors and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper. First, the local spatial information of images is incorporated into FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Second, the modification of membership partition, based on the distance between pixels within local spatial neighbors and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of membership partition. Compared with state-of-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster, since it is unnecessary to compute the distance between pixels within local spatial neighbors and clustering centers. In addition, it is efficient for noisy image segmentation because membership filtering are able to improve membership partition matrix efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.
Labeling connected components and calculating the Euler number in a binary image are two fundamental processes for computer vision and pattern recognition. This paper presents an ingenious method for ...identifying a hole in a binary image in the first scan of connected-component labeling. Our algorithm can perform connected component labeling and Euler number computing simultaneously, and it can also calculate the connected component (object) number and the hole number efficiently. The additional cost for calculating the hole number is only O(H) , where H is the hole number in the image. Our algorithm can be implemented almost in the same way as a conventional equivalent-label-set-based connected-component labeling algorithm. We prove the correctness of our algorithm and use experimental results for various kinds of images to demonstrate the power of our algorithm.
Many web-based pharmaceutical e-commerce platforms allow consumers to post open-ended textual reviews based on their purchase experiences. Understanding the true voice of consumers by analyzing such ...a large amount of user-generated content is of great significance to pharmaceutical manufacturers and e-commerce websites. The aim of this paper is to automatically extract hidden topics from web-based drug reviews using the structural topic model (STM) to examine consumers' concerns when they buy drugs online. The STM is a probabilistic extension of Latent Dirichlet Allocation (LDA), which allows the consolidation of document-level covariates. This innovation allows us to capture consumer dissatisfaction along with their dynamics over time. We extract 12 topics, and five of them are negative topics representing consumer dissatisfaction, whose appearances in the negative reviews are substantially higher than those in the positive reviews. We also come to the conclusion that the prevalence of these five negative topics has not decreased over time. Furthermore, our results reveal that the prevalence of price-related topics has decreased significantly in positive reviews, which indicates that low-price strategies are becoming less attractive to customers. To the best of our knowledge, our work is the first study using STM to analyze the unstructured textual data of drug reviews, which enhances the understanding of the aspects of drug consumer concerns and contributes to the research of pharmaceutical e-commerce literature.
Labeling of connected components in a binary image is one of the most fundamental operations in pattern recognition: labeling is required whenever a computer needs to recognize objects (connected ...components) in a binary image. This paper presents a fast two-scan algorithm for labeling of connected components in binary images. We propose an efficient procedure for assigning provisional labels to object pixels and checking label equivalence. Our algorithm is very simple in principle, easy to implement, and suitable for hardware and parallel implementation. We show the correctness of our algorithm, analyze its complexity, and compare it with other labeling algorithms. Experimental results demonstrated that our algorithm is superior to conventional labeling algorithms.
The images that are captured in sand storms often suffer from low contrast and serious color cast that are caused by sand dust, and these issues will have significant negative effects on the ...performance of an outdoor computer vision system. To address these problems, a method based on halo-reduced dark channel prior (DCP) dehazing for sand dust image enhancement is proposed in this paper. It includes three components in sequence: color correction in the LAB color space based on gray world theory, dust removal using a halo-reduced DCP dehazing method, and contrast stretching in the LAB color space using a Gamma function improved contrast limited adaptive histogram equalization (CLAHE), in which a guided filter is used to improve the artifacts of the histogram equalization. Experiments on a large number of real sand dust images demonstrate that the proposed method can well remove the overall yellowing tone and dust haze effect and obtain normal visual colors and a detailed clear image.
Conflicting results existed about the role of prognostic nutritional index (PNI) for hepatocellular carcinoma (HCC) patients who received curative hepatectomy. The aim of this study is to identify ...the predictive capacity of PNI for survival after hepatectomy.
Preoperative PNI, neutrophil-to-lymphocyte ratio (NLR), tumor feature and clinical information of 187 patients with HCC from Sir Run Run Shaw hospital were evaluated. We also conducted a meta-analysis of seven cohort studies.
Our study showed that HCC patients with a low PNI of <45 had a poor recurrence-free survival (RFS) rate (hazard ratio HR 1.762, 95% confidence interval CI 1.066-2.911, p = 0.027, respectively). The 5-year OS and RFS rates of the high PNI (≥45) vs low PNI (<45) were 76.7% vs 50.1% (p = 0.001) and 47.0% vs 28.9% (p = 0.001), respectively. In HCC TNM I patients (n = 144), a low PNI remained an independent prognostic factor of OS and RFS (HR 2.305, 95% CI 1.008-5.268, p = 0.048; HR 2.122, 95% CI 1.149-3.920, p = 0.016). The 5-year OS and RFS rates of the high PNI vs low PNI were 81.3% vs 62.4% (p = 0.041) and 53.4% vs 45.6% (p = 0.013), respectively. In the pooled analysis, the data showed that a low PNI was significantly associated with poor OS and RFS (HR 2.27, 95% CI 1.03-4.07, p < 0.001 and HR 1.68, 95% CI 1.45-1.94, p < 0.001, respectively).
The preoperative PNI was an independent prognostic factor for OS and RFS rates in HCC patients who received hepatectomy.
Diethyl aminoethyl hexanoate (DA-6), a plant growth regulator, has many beneficial effects on agricultural production. DA-6 has been applied to many plant species, but the molecular mechanism by ...which spraying DA-6 after anthesis regulates wheat grain filling is still unknown.
In this study, we used four DA-6 concentrations: C0 (0 g/L), C2 (2 g/L), C4 (4 g/L), and C6 (6 g/L). The results showed that C4 and C6 led to a significantly higher 1000-grain weight and seed protein content than C0 during two wheat growing seasons. We then subjected samples at 24 days after anthesis (at which point the grain weight increased rapidly) to transcriptome analysis. Flag leaf (L), seed (S), and stem (T) samples under C6 and C0 were used for RNA-seq. The seed samples under C6 compared with C0 (S6vsS0) presented the most differentially expressed genes (DEGs; 2164). Plant hormone signal transduction (p = 1.97 × 10
), protein processing in the endoplasmic reticulum (ER; p = 9.04 × 10
) and starch and sucrose metabolism (p = 1.90 × 10
) pathways were the most markedly enriched pathways in the flag leaves, stems, and seeds, respectively. DEGs involved in sucrose synthesis in the flag leaves, protein processing in ER in the stems, and starch synthesis and protein processing in ER in the seeds were significantly upregulated under C6 compared with C0.
Overall, we propose a model for spraying DA-6 after anthesis to regulate metabolic pathways in wheat, which provides new insights into wheat in response to DA-6.
Hepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.
We aimed to establish a robust prognostic model ...based on differentially expressed genes (DEGs) in HCC.
Using datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Genome Consortium (ICGC), DEGs between HCC tissues and adjacent normal tissues were identified. Using TCGA dataset as the training cohort, we applied the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analyses to identify a multi-gene expression signature. Proportional hazard assumptions and multicollinearity among covariates were evaluated while building the model. The ICGC cohort was used for validation. The Pearson test was used to evaluate the correlation between tumor mutational burden and risk score. Through single-sample gene set enrichment analysis, we investigated the role of signature genes in the HCC microenvironment.
A total of 274 DEGs were identified, and a six-DEG prognostic model was developed. Patients were stratified into low- or high-risk groups based on risk scoring by the model. Kaplan-Meier analysis revealed significant differences in overall survival and progression-free interval. Through univariate and multivariate Cox analyses, the model proved to be an independent prognostic factor compared to other clinic-pathological parameters. Time-dependent receiver operating characteristic curve analysis revealed satisfactory prediction of overall survival, but not progression-free interval. Functional enrichment analysis showed that cancer-related pathways were enriched, while immune infiltration analyses differed between the two risk groups. The risk score did not correlate with levels of PD-1, PD-L1, CTLA4, or tumor mutational burden.
We propose a six-gene expression signature that could help to determine HCC patient prognosis. These genes may serve as biomarkers in HCC and support personalized disease management.
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